Home
About
Sessions
Program Details
- Scientific Program
Contact

Home

About
Sessions
Scientific Program

Agenda

May 14, 2025 | 09:00 AM GMT

Webinar on Drug Discovery

Coming soon...

tomjamieson77@gmail.com

useful links

Home
About

Latest News

Free energy methods and digital transformation of drug discovery

2025-04-28 - 2025-05

Free energy predictions are well established as a robust and accurate method of triaging potential drug candidates and guiding optimisation. Now the synergistic application of AI-based generative chemistry to free energy methods promises to streamline and industrialise molecule discovery and optimisation.

Drug discovery has always been an extremely difficult field. The list of criteria that a successful drug must fulfil is often very long, and many of them are rooted in the complex interplay of chemistry with biology. As a result, the optimisation of an initial hit molecule into a clinical candidate has always been at least in part a numbers game: you keep making changes until you get lucky.

The intuition and experience of a good medicinal chemist can certainly increase the chances of success, as can the predictions from computational chemistry models, but neither of these are close to infallible. However, the adoption of free energy perturbation calculations (FEP) over the last decade or so has been game changing. One of the key properties needed in a drug molecule, the ability to bind strongly to the target protein, is now amenable to calculation with a good degree of predictive accuracy.

Free energy methods involve simulating the behaviour of a molecule both bound to a protein and solvated in water which, together with a variety of thermodynamic tricks, allow the binding constant to be either calculated directly or indirectly by comparison to a related molecule whose binding is known. These methods have a long pedigree: the theoretical foundations were put in place nearly three quarters of a century ago.1

However, there were always two major difficulties in making use of the method. The first is our modelling of chemistry: we need to be able to compute the energy of organic molecules with a high degree of accuracy, and to do so extremely quickly. Quantum mechanics is accurate but not quick, so we rely on approximate classical methods, and these require extensive parameterisation to get sufficiently accurate answers.

The second is sampling: for accurate results, you need to explore all of the different configurations that the system can take up. For a small molecule in vacuum that is not too challenging, but adding water and including a large binding partner such as a protein makes sampling much more difficult. It is only relatively recently, with the advent of fast and relatively affordable GPUs, that free energy methods have moved from specialist calculations that required significant time on a supercomputer to everyday jobs run on a handful of computers (or even a single workstation).

Drug uptake discovery could allow IV medications to be taken orally

2025-04-22 - 2025-04

A team of scientists has uncovered the mechanism of cellular uptake for large and polar drugs and devised a novel strategy to optimize the capacity of drug-delivery into these cells. The team was led by Hong-yu Li, Ph.D., professor of medicinal chemistry and chemical biology with the Department of Pharmacology and the Barshop Institute at the University of Texas Health Science Center at San Antonio (UT Health San Antonio), together with teams from Duke University (Duke) and the University of Arkansas for Medical Sciences (UAMS)

"Chemical endocytic medicinal chemistry has the potential to impact every aspect of endocytic drugs from drug discovery and development to clinical practice," said Li.

In this novel process, drug molecules are designed to better engage with CD36, a protein receptor found on the surface of many cells. By optimizing chemical interactions with CD36, the team was able to enhance the natural function of CD36, essentially increasing the gateway for larger and polar drug compounds to enter the cell.

"This innovative chemical approach can potentially make any intravenous drug able to be taken orally. It can also promote any drug crossing the blood-brain barrier. This will remarkably broaden the number of agents we have to treat brain cancer or dementia," said Robert A. Hromas, MD, FACP, dean of the Joe R. and Teresa Lozano Long School of Medicine at UT Health San Antonio.

Small-molecule drugs have been limited due to the belief that passive diffusion was the primary mechanism of cell entry. One of the most promising developments in recent years in drug discovery is induced proximity. This drug discovery process utilizes molecules to bring proteins together to create a desired protein interaction and/or chemical reaction.

Until now, molecules larger than 500 Daltons (Da) were believed to be practically unusable due to the challenges of cell access and bioavailability. This greatly restricted the kinds of compounds that could be developed as induced proximity drugs.

This new mechanistic discovery bypasses this limitation by chemically enhancing CD36-mediated uptake, amplifying the efficiency of large and polar molecules to enter target cells. CD36 was known to play a role in lipid transport and metabolism, but the team found it also had unexpected potential for promoting cellular uptake of large and polar chemical drugs.

Provocative but well-validated results

In the study, the team first discovered and validated the CD36-mediated endocytic uptake of large and polar chemical compounds with sizes between 543 and 2,145 Da and then tested the efficacy of optimized CD36 action on the cellular uptake of proteolysis targeting chimeras (PROTACs), a class of large molecular compounds that includes an E3 ligase protein-binding domain, a binding domain for a target protein, and a linker.

The team was astonished at the speed, effective uptake and potency of the compounds when utilizing the chemical endocytic medicinal chemistry strategy through CD36 interaction.

The Future of Drug Discovery: Integrating Human, AI and Robotics

2025-04-22 - 2025-04

While many pharmaceutical companies are ramping up investments in AI-powered drug discovery, Astellas has been ahead of the curve, integrating AI into its drug development processes since 2019. The company’s innovative "Human-in-the-Loop” platform is central to this effort, which seamlessly integrates human expertise, AI, and robotics to accelerate drug discovery.

This AI-driven approach, developed by a cross-functional team spanning research, platform development, and digital innovation, marks a major shift in how Astellas creates new medicines.

At Astellas, the AI drug discovery project team played a crucial role in helping researchers overcome their initial hesitance to adopt AI technology in the drug discovery process. “One of our biggest adoption challenges in the early stages of AI was figuring out how to ensure colleagues would leverage AI and effectively use it in practice” said Kenichi Mori, Lead, Modality Informatics, DigitalX. To address this challenge, the project team implemented two key approaches. The first approach involved piloting an AI-integrated platform with a small group of researchers. Their feedback helped refine the system. The second approach focused on integrating digital technology into our “Human-in-the-Loop” platform. This digital technology helped streamline tasks such as data collection and research material preparation, making these processes more efficient and significantly reducing workload for researchers.

As researchers used the platform, iterative data collection enhanced AI prediction accuracy, driving broader adoption over two years. Today, it is widely used in small- and medium-molecule drug discovery within Astellas' R&D organization. "The adoption of new technologies varies from person to person, and simply telling someone to use a new platform is not convincing enough,” said Ikumi Kuriwaki, Team Lead, Dx-Chemistry, Platform Sciences & Modalities, Oncology Research. “Breaking down silos between departments was a key factor in the project’s success and helping team members understand the technological benefits."

Drug development is a lengthy and complex journey, often taking 9 to 16 years, with a success rate for small molecules of only 1 in 23,000 in Japan.* However, by leveraging AI, Astellas employees have reduced the time needed to refine an initial compound (a hit compound) into a viable drug candidate by approximately 70% compared to traditional methods. This accelerated timeline allows Astellas to provide greater VALUE to patients around the world.

In addition, the "Human-in-the-Loop" platform has led to unexpected discoveries, with AI identifying promising compounds that may have been overlooked using traditional selection methods. This platform generates compounds along with their pharmacological activities and pharmaceutical properties in a clear format, enabling researchers to identify the most promising high-efficacy compounds with strong drug potential.

The platform also incorporates robotic automation for compound synthesis and allows for remote evaluation of the pharmacological effects of synthesized compounds. By automating repetitive tasks, Astellas researchers can dedicate more time to creative works such as deriving deeper insights from experimental results and generating innovative ideas for drug discovery.

"We have reached a point where an AI-designed, predicted and robot-synthesized compound is advancing to clinical trial,” said Kenji Negoro, Head, Platform Sciences & Modalities, Oncology Research. “By fully leveraging this platform, we have turned AI-driven drug discovery into a tangible reality. The recognition we are receiving from external institutions further validates the impact of our efforts."

Project team member Takanori Koike, Head, Drug Discovery Unit I, Innovation Labs Astellas Therapeutics Accelerator, further explains: “As AI technology becomes more prevalent, researchers have found a balance in determining which tasks to delegate to AI and which should remain in human hands. This balance has significantly enhanced the user experience and contributed to the overall success of AI implementation.”

Drug Discovery Outsourcing Market Estimated to Surpass $7.5 Billion by 2031, Growing at 7.8% CAGR | TMR Analysis

2025-04-18 - 2025-04

The global drug discovery outsourcing market, valued at US$3.6 billion in 2022, is set for robust growth over the next decade. It is projected to advance at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2031, surpassing US$7.5 billion by 2031. This expansion reflects the pharmaceutical industry's increasing reliance on contract research organizations (CROs) to streamline drug development, driven by rising R&D costs, technological advancements, and the growing prevalence of chronic diseases. As companies seek cost-effective and specialized solutions, the market is poised to more than double, solidifying its critical role in drug discovery through 2031.

Understanding the Drug Discovery Outsourcing Market

Drug discovery outsourcing involves delegating stages of drug development-such as target identification, lead optimization, and preclinical testing-to external partners like CROs and academic research organizations. This practice reduces costs and leverages specialized expertise. The 2022 valuation of US$3.6 billion underscores its established significance, with the projected rise to over US$7.5 billion by 2031 highlighting its response to the industry's need for efficiency and innovation.

The 7.8% CAGR reflects a dynamic growth phase, fueled by the complexity of modern drug development and the integration of advanced technologies. Outsourcing's role in accelerating timelines drives sustained market momentum.

Key Drivers of Market Growth

Several factors are propelling the market toward its US$7.5 billion milestone by 2031. First, escalating R&D expenditures are a primary driver. Global pharmaceutical R&D spending reached US$198 billion in 2020 and is projected to hit US$254 billion by 2026, per EvaluatePharma. High costs, averaging US$2.6 billion per approved drug, push companies to outsource to CROs, which offer cost-efficient expertise, aligning with the 7.8% CAGR.

Second, technological advancements, particularly in artificial intelligence (AI), machine learning (ML), and high-throughput screening, are transforming outsourcing. AI-driven platforms, like CytoReason's collaboration with Pfizer, enhance predictive analytics, streamlining lead identification. These innovations improve efficiency, driving the market toward US$7.5 billion by enabling faster, more accurate drug discovery.

Third, the rising prevalence of chronic and rare diseases fuels demand. Cancer, cardiovascular diseases, and orphan conditions affect millions globally, with 356,860 new cancer cases in the UK alone in 2014, per Cancer Research UK. Outsourcing supports specialized research, such as oncology trials, which surged 56% from 2016 to 2021, reinforcing the market's trajectory through 2031.

Elix and PRISM BioLab Join Forces to Accelerate AI-Driven Drug Discovery for Challenging Protein-Protein Interaction Targets

2025-04-15 - 2025-04

This partnership will accelerate research on these challenging molecular targets by combining Elix's cutting-edge AI drug discovery platform with PRISM BioLab's proprietary peptide mimetic technology for controlling protein-protein interactions.

In drug discovery, generating a clinical candidate molecule typically takes a long time and requires the synthesis of hundreds to thousands of compounds, making research and development efficiency a critical challenge.

Through this partnership, Elix's AI drug discovery platform Elix Discovery™” will be integrated with PRISM BioLab's proprietary small molecule peptide-mimetic PepMetics® technology. This collaboration will expand the scope of AI-driven exploration to include previously unconsidered small molecules, enabling the efficient identification of promising compounds for traditionally difficult targets and accelerating the creation of breakthrough drug candidates. By leveraging both companies' AI capabilities and medicinal chemistry expertise, the partnership aims to reduce time and costs, and improve the success rate of drug discovery, ultimately delivering innovative treatments for unmet medical needs.

Prior to this collaboration, Elix and PRISM BioLab conducted a pilot project in which Elix utilized AI-driven compound design strategies based on the molecular structures provided by PRISM BioLab. This collaboration successfully identified novel hit compounds that were entirely different from those achieved using conventional approaches. Building on this successful experience, the partnership aims to further enhance the efficiency of drug discovery and improve success rates through a deeper integration of both companies’ technologies.

Comments from Key Personnel

PRISM BioLab CTO Tatsuya Toma Ph.D.:
Improving drug discovery efficiency is an eternal challenge. Delivering better medicines to patients and healthcare professionals as quickly as possible requires innovation. Our PepMetics® technology has characteristics that are highly compatible with AI applications. Elix is not only a professional in AI and machine learning but also expanding their knowledge and understanding of drug discovery while maintaining a flexible corporate culture. They have shown great interest in PepMetics® features, and I believe we can advance comprehensive drug discovery research together, leveraging PepMetics® characteristics to accelerate the process.

Elix co-founder and CEO Shinya Yuki, Ph.D.:
Through a successful pilot project with PRISM BioLab, we achieved breakthrough results by creating novel compound candidates with an efficiency that would be unimaginable using conventional approaches. That experience clearly demonstrated that the combination of our companies' technologies and expertise holds tremendous potential in drug discovery. I am genuinely looking forward to this business partnership, where we can leverage our complementary strengths to pursue outcomes that neither company could achieve alone.

Elix Lead Scientist Tasuku Ishida, Ph.D.:
The development of protein-protein interaction inhibitors is highly challenging, with only a limited number of successful cases to date. PRISM BioLab is tackling this demanding field by leveraging its unique, proprietary PepMetics® technology. We are delighted that Elix's AI-driven drug discovery platform has contributed to the progress of PRISM BioLab’s project. By combining PepMetics®, which mimics key protein substructures using small molecules, and AI-powered drug discovery capability of Elix Discovery™, we will continue to make every effort to overcome drug discovery challenges that would be difficult to achieve independently.

About PRISM BioLab
PRISM BioLab is a discovery and development biotechnology company utilizing proprietary PepMetics technology to discover orally available small molecule inhibitors of protein-protein interaction (PPI) targets and transform lives of patients suffering from cancer, autoimmune, fibrosis and other diseases. PepMetics are a unique class of small molecules that mimic three-dimensional structures of alpha-helix and beta-turn, the peptide structures commonly found in intracellular PPI interphases and receptor-ligand interactions. By combining proprietary chemistry, know-how around PPI targets and AI-supported design, PepMetics® technology can deliver inhibitors of challenging PPI targets. The technology holds promise to expand the field of drug discovery by turning previously undruggable PPIs into targets readily druggable with small molecules and by generating oral small molecule alternatives for injectable biologics. PRISM BioLab is collaborating on new PPI targets with global and Japanese pharmaceutical companies. PepMetics targeting CBP/beta-catenin PPIs licensed to Eisai Co., Ltd. and Ohara Pharmaceuticals Co., Ltd. are in clinical development for cancer and liver disease, respectively.

GraphBAN: Making drug discovery faster and more affordable through artificial intelligence

2025-04-14 - 2025-04

UM researchers have developed a deep learning model to predict compound protein interactions. GraphBAN is an inductive graph-based approach. The model is all about discovering new drug candidates in the pre-clinical stage. This means speeding up the drug discovery process and making it more affordable.

"One proven approach in drug discovery is to find the proteins that play a key role in a disease or help harmful microbes survive. If we can target those proteins with the right small molecules, we can disrupt the disease process," says Hamid Hadipour, data scientist.

Hadipour conceptualized the idea and designed the algorithms along with Dr. Pingzhao Hu. Hu is an adjunct professor at UM Max Rady College of Medicine.

Hadipour explains that GraphBAN predicts if a small molecule can bind to a protein. It can also tell us which parts of it the protein interacts with. This deep-learning model speeds up the prediction process by doing a visual test using AI. It saves time and money, helping researchers focus on the best drug candidates. These can be antibiotics or cancer treatments.

GraphBAN reflects a strong interdisciplinary collaboration between chemistry, biochemistry, microbiology, and computer science. The project was made possible with Dr. Silvia Cardona's contributions and co-supervision. Cardona is a professor and associate head graduate at the Department of Microbiology. Her lab studies molecular microbiology and microbial genomics, all with a focus on antibiotic discovery.

Cardona tells us that we are going to see more AI predictions in science—predictions that we then have to confirm with experimental research. In a way, AI won't replace experimental research but rather complement it.

AI in Drug Discovery: A Game-Changer for the Pharmaceutical Industry

2025-04-12 - 2025-04

The integration of Artificial Intelligence (AI) in drug discovery is rapidly transforming the pharmaceutical industry. Traditional drug development is a long, costly, and complex process, often taking years of research and millions of dollars to bring a single drug to market. However, AI is revolutionizing this landscape, making the process faster, more efficient, and less expensive.

By leveraging machine learning (ML) and deep learning (DL) algorithms, researchers can now analyze vast amounts of data, identify patterns, and predict drug efficacy in ways that were previously unimaginable. This not only accelerates the identification of potential drug candidates but also improves the precision and safety of these drugs, leading to better outcomes in treating diseases.

AI is being applied across several stages of drug discovery. It helps in identifying novel drug targets by analyzing biological data, screening large chemical libraries, predicting the interaction between drug compounds and biological systems, and evaluating the potential side effects of drugs. By using predictive modeling, AI can also simulate how new compounds will behave in the human body, which reduces the need for extensive and costly laboratory testing. This ability to quickly identify viable drug candidates has the potential to dramatically shorten the drug discovery timeline.

A key figure contributing to this revolution is Venkata Sri Manoj Bonam, whose research focuses on enhancing the process of drug discovery by applying machine learning to predict and screen drug candidates more effectively. Bonam’s work emphasizes the use of adaptive machine learning models for real-time anomaly detection, particularly in the context of IoT-driven healthcare systems. His research aims to optimize the identification of promising drug candidates by creating AI systems that can predict the behavior of molecules and their potential therapeutic effects with greater accuracy. This kind of predictive modeling is transforming how researchers prioritize drug compounds, allowing them to make more informed decisions early in the discovery process, ultimately saving time and resources.

The application of AI in drug discovery is getting traction in a number of research laboratories aside from Bonam's lab. One such investigator is Mike Sipply, whose studies focus on deep learning algorithms that analyze genetic data to discover new drug targets for complex diseases. His AI models scan large genomic datasets for patterns and mutations that could be important for developing therapies for diseases like cancer and neurodegenerative disorders. The works of Mike Sipply extend the realms of precision medicine, allowing drugs to be designed for genetic signatures of individual patients and hence increasing effectiveness.

Meet STITCHR, the retrotransposon-based gene editor that enables large, scarless DNA insertions

2025-04-10 - 2025-04

A novel gene editing tool named STITCHR, developed at Mass General Brigham and collaborating institutions, overcomes central limitations of current technologies by enabling the scarless insertion of entire genes or large DNA fragments up to 12.7 kb. Based on reprogrammed retrotransposon activity guided by a Cas9 nickase and using RNA templates, STITCHR shows potential for developing “one-and-done” therapies for genetic diseases involving multiple mutations or requiring whole-gene replacement. In addition, its mechanism avoids double-strand DNA breaks, and its reliance solely on RNA templates may simplify therapeutic delivery strategies. A paper on the research has been published in Nature.

Co-senior author Omar Abudayyeh, PhD, an investigator at the Gene and Cell Therapy Institute (GCTI) at Mass General Brigham highlighted that CRISPR has transformed the field but added that it has limitations. “CRISPR can’t target every location in the genome, and it can’t fix the thousands of mutations present in diseases like cystic fibrosis,” Abudayyeh said in a release. “When we started our lab, one of the big things we wanted to figure out was how to insert large pieces of genes, or even entire genes, to replace faulty ones. This would allow us to target every mutation for a disease with a single gene editing construct.”

The development process began with a comprehensive computational search across animal genomes. It eventually uncovers thousands of site-specific non-long terminal repeat (nLTR) retrotransposons and reveals diverse, previously unknown insertion preferences and potential retargeting mechanisms. Profiling selected candidates biochemically and in mammalian cells identified an R2 retrotransposon from the zebra finch (R2Tg) as active. They also found that the cells were capable of being reprogrammed for scarless insertion at new genomic sites using engineered RNA payloads. To boost efficiency, the researchers fused R2Tg to a CRISPR-Cas9 nickase (SpCas9H840A). Further screening pinpointed another orthologue, R2Tocc, that had favorable characteristics like natural reprogrammability and minimal activity at its native 28S target site. Engineering a fusion of this optimized retrotransposon with the Cas9 nickase resulted in the final system, dubbed STITCHR (site-specific target-primed insertion through targeted CRISPR homing of retroelements).

The researchers reported that the STITCHR platform proved highly versatile, successfully demonstrating scarless edits ranging from single base changes up to previously mentioned kilobase sizes. It has potential for a range of uses, including gene replacement, using in vitro transcribed or synthetic RNA templates, and showing activity in non-dividing cells.

Engineering bacteriophages to produce proteins in the gut

2025-04-10 - 2025-04

Biologic drugs have opened the door to treating diseases that were once considered difficult or impossible to treat. However, unlike small molecule drugs, biologics require intravenous or subcutaneous delivery because they would otherwise degrade if they passed through the upper gastrointestinal tract. “We don’t have a good way of delivering those orally,” said Zachary Baker, a graduate student in biological sciences at Virginia Tech. “We wanted to find a way to improve upon the current oral delivery mechanisms.”

To make that happen, Baker and other researchers led by microbiologist Bryan Hsu and immunologist Liwu Li at Virginia Tech are beginning to forge a path towards oral biologic drugs that can produce therapeutic proteins directly in the human gut (1). Currently, biopharma grows microbes in large fermenters to produce biologic drugs, but Hsu pointed out that it doesn’t have to be that way. “We are our own bacterial fermenter in our gut. All we need to do is introduce the genetic material there,” he said.

Their work relies on bacteriophages, viruses that infect bacteria to replicate and then burst out from the bacteria to release their progeny. By introducing a gene for a biologic drug into the phage, they could then deliver the phage orally where it subsequently hijacks a bacterium’s resources to produce the biologic in the gut. Phages can persist in the gut for over a year, making them ideal for long-term expression of therapeutics (2).

Laurent Debarbieux, a molecular microbiologist at the Pasteur Institute who was not involved in the study, said that this was “quite an interesting concept.”

Hsu’s team began by introducing a green fluorescent protein gene into the phage genome so that they could visually track protein production once inside the gut. Then they gave the phage to mice orally and imaged their guts four days later. They found that the green fluorescent protein ended up in the mucosal lining of the gut. “That got us really excited,” said Hsu. “That's where the protein will be produced … right where we need it to be.”

Next, they used two mouse models to more closely show how their system would work to deliver disease-relevant proteins. In one model, they focused on producing the serine protease inhibitor B1a (SERPINB1a) protein, which regulates an enzyme involved in the gastrointestinal disease ulcerative colitis. When they used phages to deliver the SERPINB1a gene into gut bacteria, they saw that mice treated with the phage had lower enzyme activity and had gained more weight compared to mice that didn’t receive the gene via the phage.

In another mouse model, they tested the phage’s ability to introduce ClpB, a protein that plays a role in reducing weight gain in mouse models of obesity (3). The team fed mice a high-fat diet and then introduced the CLPB gene via phage. Afterwards, they saw that these mice reduced their food consumption.

Both of these proof-of-concept experiments suggested that protein drugs that need to act within the gut could be produced by bacteria that have been infected by phages in the lower gastrointestinal tract, avoiding degradation by the stomach. “The next challenge, which is something we'll be looking at into the future, is: How do we get those proteins that are produced in the gut and get them absorbed into the body?” said Hsu. The researchers mentioned in their publication that this could be done by altering the physicochemical properties of the drug so that it could cross the intestinal lining.

High-Throughput Screening Techniques To Improve Cellular Drug Discovery

2025-04-09 - 2025-04

Chronic diseases, such as cancer and diabetes, are a leading cause of death and disability globally. To treat these diseases, efforts for drug discovery have expanded beyond the traditional approaches of small molecules and antibodies to include cells. Among the various cell-based drug products, stem cells and immune cells rank as the most popular cell types to be investigated in clinical trials and approved by regulators.

Nevertheless, with advances in sequencing technologies, it has been discovered that even in a population of cells that are of the same type, there is heterogeneity in functions including cell-mediated cytotoxicity and paracrine secretion profiles. These variations in functions are an opportunity to discover more effective cell types as drugs to treat diseases. For instance, the ability to identify immune cells that are able to secrete more cytotoxic granzymes could make cancer immunotherapy more effective. Similarly, if we can identify mesenchymal stem cells that can secrete more growth factors, it will be highly beneficial for regenerative medicine.

For the last decade, microfluidic technologies have been extremely useful for high-throughput screening, including the selection of cells with desirable properties. They have also been integrated with artificial intelligence (AI)-based technologies for more accurate cell sorting. Beyond droplet microfluidics, where cells are sorted based on their reactions in a droplet, there is also a new movement termed “lab on a particle”. Particles are even smaller than a droplet, meaning even higher throughput and lower processing volumes. Here, we will describe the use of microfluidic methods for high-throughput screening of cell-based drug products to enhance drug discovery efforts.

Finding immune cells that kill better
Immune cell therapy, such as chimeric antigen receptor (CAR) T-cell therapy has revolutionized cancer treatment. Currently, there are six CAR T-cell therapy products approved by the US Food and Drug Administration (FDA), and more are expected to be approved in the next few years.

Immune effector cells such as cytotoxic T cells and natural killer cells are another option for cell therapies. These can mediate the killing and lysis of target cancer cells through binding of receptors in immune cells to ligands on cancer cells. The ability to identify immune effector cells that have preferential cytotoxic capabilities will guide the selection and study of these cell subtypes and improve engineering efforts to develop more effective therapies.

Quantum Computing Meets AI: D-Wave Quantum (QBTS)’s Breakthrough in Drug Discovery

2025-04-08 - 2025-04

We recently published a list of 11 AI News Updates on Wall Street’s Radar. In this article, we are going to take a look at where D-Wave Quantum Inc. (NYSE:QBTS) stands against other AI news updates on Wall Street’s radar.

China has sensed a window of opportunity in its race to usurp US dominance in AI innovation. As Washington remains focused on building regulatory walls that restrict access to AI, Beijing has resorted to fostering open-sourcing artificial intelligence models to boost AI innovation and adoption. That was clear when DeepSeek unveiled cost-effective AI models that challenged American tech dominance on large language models and data centers.

While the initial focus was on how DeepSeek came up with powerful AI models that rivaled US models, the focus has since changed to how the Chinese startup catalyzed the adoption of open-source AI models. The decision to open source AI source code comes as Chinese companies look to capitalize on growing opportunities in the artificial intelligence software market, growing at a compound annual growth rate of 35.52% and expected to be worth $223.35 billion by 2028, according to Research and Markets.

“DeepSeek’s success proves that open-source strategies can lead to faster innovation and broad adoption,” said Wei Sun, principal analyst of artificial intelligence at Counterpoint Research.

Understanding that it will require time for China to reach the level of the US in advanced AI computing, Chinese firms have concentrated on creating more efficient and cost-effective AI solutions. They are also striving to establish leading roles in open-source AI, cloud services, and worldwide data networks.

These approaches allow China to provide more affordable and unrestricted AI access to nations discontented with US policies, integrating it into developing markets in ways that will be challenging to disrupt. This is not merely a contest in AI; it is a struggle for dominance over the global digital infrastructure both now and in the future.

Since DeepSeek sent shockwaves, several Chinese companies have released open-source models that are free for individual users. The move to open-source AI models underscores a broader shift in China that no longer focuses on proprietary licensing. Instead, Chinese companies freely offer the underlying source code for modification and redistribution.

While US tech giants have always insisted on open-sourcing their models or source code, that has not always been the case in entirety, for starters, most US companies claim to use open-source code while still restricting their use and modifications. Other US companies generate revenue by charging people to access their models even on claiming they are open source.

For this article, we selected AI stocks by going through news articles, stock analysis, and press releases. These stocks are also popular among hedge funds in Q4 2024.

Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds.

Advancements in hit identification for membrane protein drug discovery

2025-04-07 - 2025-04

GPCRs are crucial drug targets, but their complex structures present significant challenges in drug discovery. In this article, Domainex’s Philip Rawlins explores how fragment-based screening and innovative technologies like PoLiPa are helping to overcome these hurdles, with a focus on the Adenosine A2a receptor.

The challenge of GPCR drug discovery

G protein-coupled receptors (GPCRs) are one of the most desirable and challenging target classes in drug discovery, as their mutation can lead to a wide range of diseases such as cancer, cardiovascular disorders and neurological conditions. GPCRs are a large family of cell surface receptors that play a key role in transmitting signals from the outside of a cell to its interior. Involved in various physiological processes, such as vision, taste, smell, immune response and neurotransmission, GPCRs are activated by various molecules including hormones, neurotransmitters and environmental stimuli, which trigger a cascade of cellular events that help regulate bodily functions. GPCRs are critical targets for drug development due to their involvement in numerous disease pathologies, with many medications working by either activating or inhibiting these receptors.

Advancements in screening technologies for small-molecule drug discovery – including cellular assays, computational screening, and biophysics-based methods enhanced by structural biology breakthroughs – have improved screening hit rates and facilitated the identification of drug candidates for previously ‘undruggable’ targets. Despite these gains, the failure rate of drug candidates in clinical trials remains troublingly high, with lack of efficacy historically being one of the primary reasons.1 This is partly due to a failure to adequately characterise the GPCR target.

Isolating GPCRs for structural characterisation is challenging as they are membrane bound, often losing their natural structure and function when isolated, making them difficult to study in vitro. They are also highly dynamic, with conformations that shift depending on their interactions with ligands. Furthermore, GPCRs are sensitive to those detergents typically used to extract membrane proteins, thus hindering the ability to isolate them. Techniques like X-ray crystallography and traditional purification methods are limited because GPCRs are inherently unstable when removed from their native membrane context, and they do not crystallise easily, making structural analysis difficult.2

Considering 25–30 percent of marketed drugs work by binding to GPCRs,3 it is vital that these challenges are overcome through innovation and research.

Fragment-based screening (FBS) has emerged as a powerful tool for discovering novel lead compounds, especially for challenging targets like the Adenosine A2a receptor.

This article explores the potential of fragment-based screening in the GPCR landscape through the lens of the Adenosine A2a receptor, showcasing the development of methods that could help to overcome historical roadblocks in GPCR drug discovery.

GPCRs and their therapeutic relevance
GPCRs are the largest family of membrane receptors in humans, with the genes for GPCRs accounting for over three percent of human genes. They play a critical role in mediating a wide array of bodily processes and signalling pathways3 and are thus highly sought-after drug discovery targets due to their involvement in key signalling pathways.

The Adenosine A2a receptor plays an important role in many biological functions, such as cardiac rhythm, coronary circulation, immune function, pain regulation, sleep and preventing tissue inflammation. This receptor is also expressed in the brain, where it helps to regulate dopamine release, making it a therapeutic target for the treatment of insomnia, pain, depression and Parkinson’s disease. This crucial role in regulating vital bodily processes makes it a highly important drug target within GPCR research.

OICR funds five innovative cancer drug discovery projects

2025-04-03 - 2025-04

The Ontario Institute for Cancer Research (OICR) has announced its support for five Ontario research teams working to develop the next generation of medicines that kill tumours more effectively, cause fewer side effects and reduce the risk that cancer will come back.

The projects will be funded as part of OICR's Cancer Therapeutics Innovation Pipeline (CTIP) awards, which provides research teams with up to $300,000 over two years to help advance promising drug discovery research so that new cancer drugs can more quickly and safely reach patients.

CTIP applications are reviewed by a committee of experts from academia and industry, who also provide scientific and strategic advice to the funded research teams.

The five new CTIP research teams span the province from Thunder Bay to Toronto. They are taking innovative approaches to treating cancer by harnessing new insights about cancer biology to help stop cancer from spreading, reduce unwanted side effects and overcome treatment resistance.

This year's CTIP recipients include:

Dr. Jinqiang Hou (Lakehead University, Thunder Bay Regional Health Research Institute (TBRHRI)) and Dr. Guillem Dayer (TBRHRI) who will explore a potential treatment for cervical cancer, the world's third most common cancer among women aged 20-39. By targeting a protein found only in cervical cancer cells, they will develop a drug to find and kill cancer cells, with limited damage to surrounding healthy tissue.

"Our hybrid molecule, acting like a guided missile that targets only cancer cells, could provide a new tool to destroy cancer while minimizing side effects for the patient." – Dr. Jinqiang Hou, Associate Professor (Chemistry), Lakehead University, Scientist, TBRHRI
Dr. Iacovos Michael (Sunnybrook Research Institute) and Dr. Masoud Vedadi (OICR) who will investigate ways to overcome cancer metastasis and resistance to treatment, the two main reasons patients ultimately die of cancer. With CTIP funding, they will build on recent discoveries about a protein that plays a key role in metastasis and resistance for multiple types of cancer, further study its role in cancer development, and explore ways to impede its function.

"Understanding how this protein works will allow us to develop drugs that hinder its function, with the ultimate goal of improving the survival and quality of life of patients affected by cancer." – Dr. Iacovos Michael, Scientist (Biological Sciences), Sunnybrook Research Institute
Dr. Valentina Evdokimova (OICR) and Dr. Laszlo Radvanyi (University of Toronto) who will harness the "dark matter" of the human genome to identify endogenous retroviruses (ERVs) which could be targeted to prevent cancer progression and immunosuppression. Their team's goal is to develop a validated drug screening platform that can help unlock the potential of ERV targeting in cancer drug discovery and to use that platform for testing potential therapeutic candidates.

"Though human endogenous retroelements were considered disabled and functionally inept for decades, we are now showing that they are highly expressed in cancerous cells and may hold exciting potential for therapeutics to stop the development of cancer or prevent it altogether." – Dr. Valentina Evdokimova, Research Scientist, OICR

Mount Sinai Launches AI Small Molecule Drug Discovery Center

2025-04-02 - 2025-04

New York, NY (April 02, 2025) The Icahn School of Medicine at Mount Sinai has launched the AI Small Molecule Drug Discovery Center, a bold endeavor that harnesses artificial intelligence (AI) to revolutionize drug development. The new Center will integrate AI with traditional drug discovery methods to identify and design new small-molecule therapeutics with unprecedented speed and precision.
Unlike conventional drug discovery, which can take years and cost billions, AI-driven approaches enable researchers to rapidly navigate a vast chemical landscape, including natural products, to pinpoint promising drug candidates. By leveraging Mount Sinai’s world-leading expertise in machine learning, chemical biology, and biomedical data science, the Center aims to bring innovative treatments to patients faster—particularly for diseases with urgent unmet needs, including cancer, metabolic disorders, and neurodegenerative diseases.

“At Mount Sinai, we are dedicated to redefining the future of medical innovation,” says Avner Schlessinger, PhD, Professor of Pharmacological Sciences and Associate Director of the Mount Sinai Center for Therapeutics Discovery at the Icahn School of Medicine at Mount Sinai, who will lead the Center. “By integrating artificial intelligence with cutting-edge chemistry and biology, we can dramatically accelerate drug discovery and develop new treatments for some of the most complex and pressing diseases.”

The AI Small Molecule Drug Discovery Center will focus on three core areas: designing novel drug-like molecules using generative AI, optimizing existing compounds to enhance their efficacy and safety, and predicting drug-target interactions to repurpose known drugs or natural products for new indications.

Experts in computational drug design at the new center could revolutionize the drug discovery process by transforming traditional rational drug design approaches by guidance from AI-driven predictions. By training AI models on vast datasets of molecular structures and biological activity, they can anticipate the properties of new compounds before synthesis, potentially saving years of experimental work.

A major strength of this AI-powered strategy lies in its capacity to explore chemical space on a scale far beyond human capability. “Vast combinatory possibilities in optimal drug design lead to high costs, lengthy timelines, and low success rates,” says Ming-Ming Zhou, PhD, Dr. Harold and Golden Lamport Professor in Physiology and Biophysics and Chair of the Department of Pharmacological Sciences at the Icahn School of Medicine at Mount Sinai. “AI efficiently navigates this complex landscape, pinpointing the most promising candidates—an achievement unimaginable just a few years ago.”

The AI Small Molecule Drug Discovery Center will also foster collaborations with leading pharmaceutical companies, biotech firms, and academic institutions to drive drug development. Additionally, it provides hands-on training for the next generation of scientists through seminars, internship programs, and AI-driven drug discovery hackathons.

The new Center builds on other major recent Mount Sinai AI initiatives, including the launch of its new AI building and the recent announcement of the Center for Artificial Intelligence in Children’s Health.

“AI is fundamentally reshaping how we understand and target disease at a molecular level," says Alexander Charney, MD, PhD, Vice Chair, Windreich Department of Artificial Intelligence and Human Health, and Associate Professor of Artificial Intelligence and Human Health, Psychiatry, Genetics and Genomic Sciences, and Neuroscience at the Icahn School of Medicine at Mount Sinai. He is also Director of The Charles Bronfman Institute for Personalized Medicine. "By integrating artificial intelligence with genetic insights, we can move beyond conventional drug discovery to design precision therapeutics tailored to the underlying biology of neuropsychiatric and many other complex disorders.”

The center is guided by a distinguished Scientific Advisory Board, composed of leading experts in drug discovery and machine learning:

Jian Jin, PhD, Mount Sinai Professor in Therapeutics Discovery and Director of the Mount Sinai Center for Therapeutics Discovery at the Icahn School of Medicine at Mount Sinai, a renowned expert in synthetic chemistry and drug development with extensive experience spanning both academia and industry.
Dr. Zhou, who also serves as Co-Director of the Drug Discovery Institute and Professor of Oncological Sciences. His research focuses on the mechanisms of gene transcription in health and disease, with a strong emphasis on epigenetic drug discovery.
Marta Filizola, PhD, Sharon and Frederick Klingenstein-Nathan Kase, MD Professor and Dean of the Graduate School of Biomedical Sciences, a leading authority in the computational biophysics of membrane proteins. With more than 25 years of experience, she has pioneered the use of AI and theoretical chemistry to advance drug discovery and development.
Girish Nadkarni, MD, MPH, CPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, where he is also Director of the Hasso Plattner Institute for Digital Health and Irene and Dr. Arthur M. Fishberg Professor of Medicine. A trailblazer in AI and digital health, he founded several companies and has several patents related to AI.
The Center will initially focus on building an advanced AI infrastructure and initiating key drug discovery projects. Over the next two years, Mount Sinai expects to make significant breakthroughs in AI-driven drug design, further cementing its role as a leader in biomedical innovation.

"The launch of the AI Small Molecule Drug Discovery Center reflects our commitment to pushing the boundaries of biomedical innovation,” Eric J. Nestler, MD, PhD, Director of The Friedman Brain Institute, Dean for Academic Affairs, and Nash Family Professor in the Nash Family Department of Neuroscience at the Icahn School of Medicine at Mount Sinai, and Chief Scientific Officer of the Mount Sinai Health System. “By harnessing the power of AI, we are transforming the way new medicines are discovered and developed, bringing hope to patients who need breakthrough treatments faster than ever before."

“We’re at the dawn of a new era in drug discovery,” Dr. Schlessinger added. “By combining AI, computational chemistry, and biomedical research, we’re not just making drug discovery faster—we’re making it smarter, more efficient, and more tailored to the biological complexity of human disease.”

Google DeepMind’s drug discovery spin-off Isomorphic Labs raises $600mn

2025-03-31 - 2025-04

Isomorphic Labs, the drug discovery start-up spun out of Google’s DeepMind artificial intelligence unit, has raised $600mn in its first external funding round, as investors bet on the potential for AI to “solve disease”.

The fundraising for the London-based group, whose chief executive is Nobel Prize winner Sir Demis Hassabis, was led by Thrive Capital, the New York-based investor that is also among the biggest backers of ChatGPT maker OpenAI.

The round, announced on Monday, was also joined by GV, the venture capital arm of Alphabet, Google’s parent company. It also included further investment from Alphabet.

Further financial details, such as Isomorphic Labs’ valuation and its new ownership structure, were not revealed. However, Alphabet remains the majority shareholder.

The fundraising is among the biggest for a UK AI company, providing a financial boost as Hassabis pushes to have drugs designed by AI in clinical trials by the end of the year.

“This funding will further turbocharge the development of our next-generation AI drug design engine, help us advance our own programmes into clinical development and is a significant step forward towards our mission of one day solving all disease with the help of AI,” he said.

The company said the investment would help accelerate AI research and development in drug design and would allow the company to expand its team.

Josh Kushner, founder and chief executive of Thrive Capital, said: “We believe Isomorphic has earned a rare position to define a new age of drug discovery and design, and we are deeply inspired by their mission and the extraordinary progress they have made to date.”

Isomorphic Labs was spun out from DeepMind in 2021 and has since partnered with large pharmaceutical companies, such as Novartis and Eli Lilly, which are keen to cut costs and develop drugs faster.

DeepMind in May unveiled AlphaFold 3, an AI model that can predict the structures of genetic code DNA and RNA, as well as ligands, which are molecules that bind to others and that can be important markers of disease. Isomorphic Labs’ researchers use the tool as well as proprietary AI models to run predictions and simulate drug development.

In the company’s first year of full operations in 2023, it reported losses of £60mn, up from £17mn a year earlier, according to its latest accounts filed at Companies House. Isomorphic Labs also reported it R&D costs grew to £49mn from £12mn, and tripled its staff costs to £20mn with a headcount of 71.

Hassabis, who jointly received the Nobel Prize for chemistry last October for developing AlphaFold, an AI tool that can predict the structure of proteins, said on LinkedIn: “I’ve long felt that improving human health is the most important thing we can do with AI and today marks a big step towards a new era of drug discovery.”

New drug OLX-07010 shows promise in treating tau diseases

2025-03-31 - 2025-03

OLX-07010, a new drug from Oligomerix, shows potential in reducing tau protein tangles, a key factor in Alzheimer’s and other tau-related diseases. Early studies suggest it could be a breakthrough in targeting tau aggregation and improving brain function.

A promising new treatment from New York-based biotech Oligomerix is offering hope for the future of tau-related disease therapies. OLX-07010, an oral small molecule, has been shown to reduce the tangling of tau protein in the brains of mice modelling a rare neurological disorder, suggesting it could be a viable treatment for diseases like Alzheimer’s diease (AD).

The results, published in the Journal of Neurochemistry, indicate that OLX-07010 could effectively target tau-related diseases. In previous studies, Oligomerix researchers found that OLX-07010 prevented tau aggregation in mice genetically predisposed to develop tau tangles. This new study, however, focused on mice that already had tau aggregations. Treatment with OLX-07010 was shown to reduce these harmful tau bundles compared to untreated mice.

The role of tau in neurodegenerative diseases
Tau protein is crucial for stabilising neurons in the brain. However, in certain neurological diseases, it can form sticky aggregates that interfere with brain function. In this study, the mice had a mutated form of tau resembling that found in progressive supranuclear palsy (PSP), a rare tauopathy that causes difficulties with speech, coordination, and memory.

While tau plays a significant role in Alzheimer’s disease, the tau in this condition is not mutated in the same way as in PSP. As a result, the authors highlight the need for further research to investigate how OLX-07010 affects mice with Alzheimer’s-like tau tangles.

Improved motor coordination
In addition to reducing tau tangles, the treated mice demonstrated enhanced motor coordination. In a common test for motor function, the rotating rod, the treated mice were able to maintain balance for longer than their untreated counterparts. This suggests that OLX-07010 not only reduces tau accumulation but also improves neurological functions.

Potential for a synergistic Alzheimer’s treatment
In Alzheimer’s disease, both tau and amyloid beta protein aggregation are thought to contribute to disease progression. Dr William Erhardt, President and Head of Development at Oligomerix, emphasised the potential of combining drugs that target amyloid beta aggregation – considered an initiating factor in Alzheimer’s disease – with treatments like OLX-07010 that target tau and inflammation.

He said: “This study shows that treatment of mice with pre-existing tau aggregates with OLX-07010 can block the further accumulation of tau aggregates. This is important for Alzheimer’s patients because the progression of tau aggregation begins years before clinical symptoms are apparent.” He continues, “It is anticipated that drugs targeting amyloid beta aggregates, considered an initiating factor for AD, with drugs targeting tau and inflammation, involved in disease progression, should have a synergistic effect for treating AD.”

Current clinical trials
OLX-07010 is currently being tested in a Phase I clinical trial involving healthy, elderly volunteers. The trial, which began in February 2023, is expected to be completed later this year. With these promising preclinical results, OLX-07010 may soon offer a new approach to treating tau-related neurological diseases.

Predictive Oncology (POAI) Uses AI to Slash Drug Discovery Timelines & Boost Cancer Research

2025-03-27 - 2025-03

We recently published a list of Top 8 AI News Updates Driving Markets Today. In this article, we are going to take a look at where Predictive Oncology Inc. (NASDAQ:POAI) stands against other top AI news updates driving markets today.

The AI race switched gears after Chinese AI startup DeepSeek announced on March 25th that it released a major upgrade to its V3 LLM, intensifying competition with US rivals like OpenAI and Anthropic.

The new model, DeepSeek-V3-0324, is available through the AI development platform Hugging Face as part of the company’s latest efforts to position itself as a leader in the global AI market. DeepSeek claims that the upgrade brings major improvements in reasoning and coding capabilities.

Elsewhere, fintech Ant Group, backed by Jack Ma, claimed that it was able to develop methods using Chinese chips that trim AI training costs by 20%. This update could hint that Chinese AI developers might be able to gradually reduce reliance on top AI chips manufactured in the US.

We selected AI stocks by reviewing news articles, stock analysis, and press releases. We listed the stocks in ascending order of their hedge fund sentiment taken from Insider Monkey’s Q4 2024 database of over 1000 hedge funds.

Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter’s strategy selects 14 small-cap and large-cap stocks every quarter and has returned 373.4% since May 2014, beating its benchmark by 218 percentage points (see more details here).

Predictive Oncology Inc. (NASDAQ:POAI) leverages AI and ML to speed up drug discovery and enable drug development to benefit cancer patients. The company claims that its AI platform, PEDAL, can predict the efficacy of medicines on tumor samples with 92% accuracy, which helps in the informed selection of drug/tumor type combinations for in-vitro testing.

On March 25th, Predictive Oncology Inc. (NASDAQ:POAI) said it used its active machine learning platform in partnership with the Natural Products Discovery Core (NPDC) at the University of Michigan Life Sciences Institute to develop predictive models derived from 21 unique compounds. The company said that tumor types — breast, colon and ovary — were selected for testing with the compounds and a well-known anti-cancer drug. After measuring 7% of the possible wet lab experiments, the company’s predictive ML model made confident predictions to cover 73% of all experiments, virtually eliminating up to two years of laboratory testing.

Overcoming delivery hurdles, IL-12 tackles ovarian cancer

2025-03-27 - 2025-03

Deep within the abdomen, on either side of the uterus, sit the ovaries. Due to their small size and location, if cancer develops there, it is very difficult to detect — often leading to a diagnosis only at advanced stages. This makes ovarian cancer the deadliest gynecological cancer; more than 60 percent of women die within five years of a late-stage diagnosis (1).

Immune cells in the region around the tumor, however, play a key role in the cancer’s progression.

“Ovarian cancer is just like a lot of cancers: The better your immune system is, hopefully the better it can take care of the cancer,” said Premal Thaker, a gynecologic oncology clinician and researcher at Washington University in St. Louis. Researchers have seen that when immune cells do manage to infiltrate the ovarian tumor microenvironment, patients have better outcomes (2).

Giorgio Valabrega, an ovarian cancer researcher at the University of Turin, explained that this finding encouraged multiple research groups to develop immunotherapies for ovarian cancer. The majority of them were immune checkpoint inhibitors, which aid the immune system by helping T cells recognize tumor cells that try to evade detection.

The initial excitement around these immunotherapies for ovarian cancer, however, has dimmed. Clinical trial after clinical trial with these drugs showed little to no improvement for patients over the standard of care, which for late-stage ovarian cancer is first chemotherapy to shrink the tumor, followed by surgery to remove as much of the tumor as possible, and then another round of chemotherapy to treat any remaining cancer cells (3).

But now, researchers at Imunon, a biotechnology company developing DNA-based immunotherapies, may have found a way forward with their new drug IMNN-001. It harnesses interleukin-12 (IL-12), a powerful immune stimulator, to help patients’ immune systems fight against advanced ovarian cancer. With positive Phase 2 data, the Imunon team thinks that they may even have a new first-line treatment for ovarian cancer on their hands.

“We really haven't seen a change to the standard of care, to first-line treatment, for 25 years,” said Stacy Lindborg, the President and Chief Executive Officer of Imunon. “For no progress for this long, it leaves us with a lot of hope.”

For years, scientists have known that IL-12 is a powerhouse when it comes to whipping up a strong immune response against cancer (4). It activates tumor-fighting cytotoxic CD8+ T cells and natural killer cells, promotes the production of interferon gamma to decrease the tumor’s blood supply, inhibits regulatory T cells that suppress the immune response, and leads to the production of other immunostimulatory molecules (5).

“It just has this multi-prong attack,” said Thaker, adding that it has “different ways of augmenting the immune system, and that's really critical.”

Despite its cancer-fighting strengths, there are currently no IL-12-based therapies. The problem, it turned out, was that IL-12 is a little too strong. While IL-12 led to positive responses in patients in clinical trials, high systemic levels of IL-12 also caused intolerable liver and hematological toxicities (6).

“A lot of the promise of IL-12 … was very well recognized and very well appreciated, also amongst Big Pharma, but nobody had figured out how to safely deliver it,” said Lindborg.

During this time, however, researchers led by Khursheed Anwer, now the Executive Vice President and Chief Science Officer of Imunon, were developing a solution. Rather than deliver IL-12 systemically, Anwer and his team formulated a DNA plasmid encoding the human IL-12 gene into a nanoparticle made of a synthetic lipopolymer (7). This gene therapy was originally named GEN-1, but Imunon has now renamed it to IMNN-001.

“It's a very unique gene therapy in the sense that all of the approved gene therapies are viral. This is a non-viral gene therapy,” said Lindborg. “These DNA plasmids are encoded to basically instruct the body to produce this immune response.”

Scientists at Newcastle University create a bioprinting method to speed up drug discovery

2025-03-25 - 2025-03

Newcastle University scientists, supported by funding from Versus Arthritis, have created a bioprinting method that produces cell-filled gels closely resembling human tissues, earning patents in both the US and Europe.

The Reactive Jet Impingement (ReJI) bioprinting technique involves jetting two different liquids at each other—one containing cells in a cross-linking solution and the other a polymer solution. These solutions combine mid-air to create a cell-filled hydrogel that can be printed on nearly any surface. This approach aims to enhance cell density to approximately ten times that of conventional bioprinting technologies and operates at a much faster rate. The resulting tissues are significantly more like human tissue samples.

Their discovery shows such potential that they have launched a spin-out company, Jetbio, to attract investment and introduce the ReJI printer to laboratories worldwide.

Professor Dalgarno, of Newcastle University’s School of Engineering, said, “Drug discovery is a complicated and extremely costly process involving multiple rounds of testing before they reach clinical trials. In clinical investigations, only one in ten of the compounds tested proceeds to reach the market. These rates of failure make it clear that we must improve our models so that they are more representative of drug response in humans. There is currently a lot of interest in developing better human in vitro models of diseases and tissues so we have better ways of testing drugs.”

The landscape of drug discovery is changing and interest in new technology is growing. With the potential for the British start-up to tap into a rapidly expanding global market, the Jetbio team was invited to showcase the technology to ministers and senior figures in public health.

The changing landscape of drug discovery

Lucy Donaldson, director of research at Versus Arthritis which funded the research through the Tissue Engineering and Regenerative Therapies Centre Versus Arthritis, said, “The JetBio team are in the vanguard of research driving forward new technologies that promise to improve both the quality and speed of drug development. These advances can potentially bring new drugs to the population sooner – and that applies to treatments for arthritis, cancer, and cardiovascular disease. This is a very exciting leap forward.”

A crucial step in the drug development process is testing on in-vitro cell cultures, which traditionally involve growing cells on a flat surface, such as a microscope slide. In vitro refers to tests or processes conducted outside the body under artificial conditions, like on a slide or in a test tube. However, these 2D models do not accurately reflect the human body, where cells naturally interact with each other in a 3D environment.

Introducing Jetbio’s technology into labs to print cells in a 3D matrix, which more closely replicates human tissues, could significantly improve testing accuracy. This advancement has the potential to transform what is currently a lengthy process with high failure rates, explained Professor Dalgarno from Newcastle University’s School of Engineering.

The coffee machine-sized printers also hold promise for regenerative medicine. Arthritis, which affects 10 million people in the UK and over 500 million globally, currently has limited treatment options. This condition significantly impacts people’s ability to lead pain-free, active lives. For arthritis patients, the goal is to refine the technology to enable faster and more personalised cell culturing for use in Autologous Chondrocyte Implantation (ACI), a procedure where surgeons repair cartilage damage by implanting specialized cells into cartilage defects. This technology could enhance the precision and success rates of such implants.

The printers also have potential applications for a wide range of medical conditions. A notable example is the EC-funded REBORN project, where the Newcastle team is developing an in vitro heart chamber model. By combining ReJI bioprinting with other bioprocessing techniques, they aim to create a tissue-engineered “sleeve” that can be connected to small pumps to simulate a beating heart. The sleeve’s unique structure contains cardiomyocyte and fibroblast cells, mirroring those found in heart tissue, and is being developed to support the testing of new treatments for cardiac conditions.

Funding from the NC3Rs has supported showcasing the printers to the scientific community through workshops held in Bristol, Newcastle, and Cambridge in February. The printers are now set to be deployed in the labs of these three universities.

New light-driven process could accelerate the discovery of new drugs

2025-03-23 - 2025-03

Researchers at Indiana University and Wuhan University in China have unveiled a groundbreaking chemical process that could streamline the development of pharmaceutical compounds, chemical building blocks that influence how drugs interact with the body. Their study, published in Chem, describes a novel light-driven reaction that efficiently produces tetrahydroisoquinolines, a group of chemicals that play a crucial role in medicinal chemistry.

Tetrahydroisoquinolines serve as the foundation for treatments targeting Parkinson's disease, cancer, and cardiovascular disorders. These compounds are commonly found in medications such as painkillers and drugs for high blood pressure, as well as in natural sources like certain plants and marine organisms.

Traditionally, chemists have relied on well-established but limiting methods to synthesize these molecules. The new research, co-authored by Kevin Brown, the James F. Jackson Professor of Chemistry in the College of Arts and Sciences at Indiana University Bloomington, and Professors Xiaotian Qi, Wang Wang, and Bodi Zhao of Wuhan University, presents a fundamentally different approach.

How it works: Light as a chemical tool
Instead of using traditional chemical reactions, scientists harness light to trigger a process called photoinduced energy transfer, wherein light initiates a controlled reaction between sulfonylimines (a type of chemical compound) and alkenes (another type of compound), leading to the creation of tetrahydroisoquinolines-a type of complex molecule. This method allows for the development of new structural patterns in the molecules, which were previously difficult or impossible to create using other methods, offering a more efficient way to make complex molecules.

Brown and colleagues also found that tiny changes in the location of electrons within the starting materials had a huge impact on how the reaction played out-akin to if these electrons were puzzle pieces that needed to fit together just right. By tweaking the shapes of these pieces, the scientists made sure that only the desired product was formed, making the process highly selective. This is crucial for making medicines, where even a small mistake in a molecule's structure can turn a helpful drug into something useless or even harmful.

Implications for medicine and other industries
"The ability to create a wider range of tetrahydroisoquinoline-based molecules means that medicinal chemists can now explore new drug candidates for treating diseases like Parkinson's, certain types of cancer, and heart conditions," noted Professor Qi. "Right now, some diseases have very few effective treatment options, and this method could help scientists discover new and better drugs more quickly."

Beyond pharmaceuticals, this research could also impact other industries that rely on fine chemicals. In agriculture, for example, similar chemical reactions could be used to develop more effective pesticides or fertilizers. In materials science, it could help create new synthetic materials with specific properties, such as better durability and longevity and greater resistance to heat for the aerospace, automotive, electronics, and medical industries.

The researchers plan to fine-tune the reaction conditions, meaning, they will experiment with different ingredients and settings to make the improve the process further. They also aim to find out if this method can work on even more types of molecules, expanding its usefulness. In addition, they hope to partner with pharmaceutical companies to test whether this technique can be used to produce medicines, potentially leading to new drug discoveries that could make a difference in people's lives.

"This approach gives chemists a powerful new tool," said Professor Brown. "We hope especially it will open the door to the development of new and improved therapies for patients around the world."

As the field of photochemistry continues to expand, innovations like this may redefine how medicines and essential chemicals are made, paving the way for faster, cleaner, and more efficient production methods.

Google releases new AI models for drug discovery

2025-03-21 - 2025-03

Google gave an update on how it is using AI to advance science and build products at its recent Check Up event.

In a blog post, Google's chief health officer, Dr. Karen DeSalvo, said that since the company launched AI Overviews last year, people are more satisfied with their search results and are asking longer, more complex questions.

AI Overviews appear in Google Search results to allow people to find general information about their inquiry from a range of sources and discover relevant sites on the web.

"And with recent health-focused advancements on Gemini models, we continue to further improve AI Overviews on health topics so they’re more relevant, comprehensive and continue to meet a high bar for clinical factuality," DeSalvo said.

Google also announced the launch of its new medical records APIs globally in Health Connect.

The APIs allow apps to read and write medical record information like allergies, medications, immunizations and lab results in standard Fast Healthcare Interoperability Resources (FHIR) format.

With these additions, Health Connect supports more than 50 data types across activity, sleep, nutrition, vitals and medical records.

Last month, DeSalvo highlighted how the company received FDA clearance for its Loss Of Pulse Detection feature available on Pixel Watch 3.

The feature can detect when there is a loss of pulse due to cardiac arrest, respiratory or circulatory failure, overdose or poisoning. It automatically prompts a call to emergency services.

The feature is available in 14 countries and will be rolled out in the U.S. at the end of March.

Additionally, with the aim of helping biomedical researchers create novel hypotheses and research plans, Google launched an AI co-scientist, a new system built on Gemini 2.0.

The aim of the AI co-scientist is to help researchers parse large volumes of scientific literature and generate high-quality, novel hypotheses.

"For instance, let’s say researchers want to better understand the spread of a disease-causing microbe. They can specify this research goal using natural language, and the AI co-scientist will propose testable hypotheses, including a summary of relevant published literature and a possible experimental approach," DeSalvo said.

The collaborative tool is also aimed at helping experts uncover new ideas and accelerate their work.

"We’re already working with partners, including Imperial College London, Houston Methodist and Stanford University, and are keen to see how researchers around the world use this tool. And while it’s in its early days, the enthusiasm is clear, we’ve received considerable interest for our upcoming trusted tester program," DeSalvo said.

Google also announced TxGemma, a collection of Gemma-based open models intended to improve the efficiency of AI-powered drug discovery.

"The development of therapeutic drugs from concept to approved use is a long and expensive process, so we’re working with the wider research community to find new ways to make this development more efficient," DeSalvo said.

She pointed out that TxGemma has the ability to understand regular text and the structures of different therapeutic entities that include small molecules, chemicals and proteins.

"This means researchers can ask TxGemma questions to help predict important properties of potential new therapies, like how safe or effective they might be," DeSalvo said.

Finally, with Google's assistance, the Princess Máxima Center for pediatric oncology in the Netherlands is developing an AI tool called Capricorn that utilizes Gemini models to help physicians accelerate the identification of personalized cancer treatments by combining vast public medical data and de-identified patient data.

According to DeSalvo, Capricorn quickly generates summaries of treatment options and relevant medical publications that allow physicians to have more thorough discussions on how to attain the best health outcomes for their pediatric patients.

THE LARGER TREND

In February, Google announced a partnership with the Institute of Women's Cancers, founded by France's cancer research and treatment center, Institut Curie, to study how AI tools can help address cancer, share science-based health information and support postdoctoral researchers with funding.

The partnership, which was announced at the France AI Action Summit, combines Institut Curie’s proficiency in research and medicine with Google's advanced AI capabilities.

Primary care booking platform Healthengine also added a new booking pathway in partnership with Google. The Reserve with Google feature permits patients to book appointments directly from a practice's Google Business profile upon search.

NVIDIA GTC: AI Innovation Driving Change in Healthcare and Drug Discovery

2025-03-21 - 2025-03

Artificial intelligence (AI) is revolutionising healthcare, with industry leaders highlighting its growing impact at a recent GTC event.

Panellists from various fields discussed how AI is improving mental health care, drug development, and veterinary support.

Dr Chelsea Sumner, Nvidia's healthcare startups lead, moderated the discussion and introduced the key themes of the panel: create, cure, and care. Each expert then shared personal insights into how AI is reshaping healthcare innovation.

Alessandro, founder and CEO of Therapy Side, explained how his AI-driven mental health platform improves therapy outcomes. "I would create an AI-powered ecosystem where therapists can access both therapy session information and real-world data like wearables or mood trackers," he said. He added that technology is no longer the primary barrier — psychological resistance is now the main hurdle.

Connie De Cruz, CEO and founder of Superluminal Medicines, highlighted the challenge of fragmented data in drug discovery.

"We're faced with an extraordinary amount of siloed information that's not shared," he said. De Cruz advocated for sharing non-competitive, preclinical data to improve AI models, adding, "What we need is a particular compound structure data type that we can build models on."

Davide, Chief Technology Officer for Item, described the impact of their AI tool, Laca, which aids veterinarians in reducing stress and improving patient care. "Veterinarians are under significant pressure and have a higher probability of suicide," he explained. "Laca supports them by offering clinical advice, confirming their conclusions, and even guiding how to communicate with pet owners."

Dr Shai Shen-Orr, systems biologist and co-founder of Cyto Reason, emphasised the human factor as a key barrier to progress in healthcare AI.

"The limiting factor is actually much more on the human side," he said. "Overcoming organisational resistance and ensuring companies see the value of AI integration will determine who leads in healthcare innovation."

The panel also explored the use of generative AI in healthcare. De Cruz detailed how Superluminal uses AI to model dynamic protein structures and accelerate drug discovery. "What used to take us three months now takes just 24 hours," he said.

Shin highlighted how AI is helping Cyto Reason tackle inefficiencies in drug development.

"The amount of data in biology is exploding, yet insight is not increasing at the same pace," he said. "We've implemented AI systems that provide 30 times greater scalability when processing complex biological data."

The conversation also touched on mental health innovation. Alessandro described how Therapy Side's AI tool, Maya, streamlines therapists' administrative tasks and improves patient care.

"Therapists using our platform save up to 20 minutes per session," he said. "This enables them to expand their patient portfolio by up to 40%."

The discussion concluded with a focus on the importance of collaboration.

Davide explained how partnerships with universities and stakeholders have been crucial to Item's progress. "We engaged with over 100 clinics and hospitals during development," he said. "Feedback helped us refine our platform to better serve veterinarians."

Sumner wrapped up the session by encouraging businesses to embrace AI-driven healthcare solutions. "If we want to create, innovation has to be scalable," she said. "If we want to cure, we need data — and that data must be shared. Ultimately, collaboration is how we show we care."

"It's about ensuring that the human element remains central to innovation," she concluded.

Sapio Sciences makes AI-Native drug discovery seamless with NVIDIA BioNeMo

2025-03-20 - 2025-03

Sapio Sciences, the science-aware™ lab informatics platform, today announced the integration of the NVIDIA BioNeMo platform into the Sapio Lab Informatics Platform. This integration brings AI-driven computational drug discovery directly into Sapio ELN (Electronic Lab Notebook), helping to streamline workflows and improve decision-making in drug discovery. With the BioNeMo platform, researchers can accelerate drug discovery by leveraging science-specific AI frameworks, pre-trained models, and generative AI tools to streamline the identification of potential drug candidates and improve target selection accuracy. NVIDIA BioNeMo provides scientists with a framework for training and deploying large biomolecular language models at supercomputing scale.

The integration of BioNeMo into the Sapio Platform provides scientists with embedded in silico tools, enabling them to, for example, generate novel candidate molecules early in the research process and test their docking with a target protein. With AI-driven molecular simulations available within a single, unified workflow, researchers can streamline their processes, enhance innovation, and accelerate the transition from discovery to development.

Within Sapio ELN, researchers can access BioNeMo NIM microservices to rapidly identify and optimize drug candidates using AI-driven molecular modeling, including AlphaFold2 NIM for predicting accurate 3D protein structures; MoIMIM NIM to enable the design and optimization of small molecules; and DiffDock NIM, an AI-powered docking model developed by MIT. These models provide scientists with easy access to AI-driven tools without extensive setup, ensuring faster, more efficient research workflows.

Kevin continued: “We are working with NVIDIA to equip scientists and researchers with the most advanced AI tools to drive innovation in life sciences. This collaboration is a major step toward making AI an integral part of the drug discovery process, helping researchers make faster, data-driven decisions and improve research outcomes.”

Anthony Costa, Director, Digital Biology at NVIDIA, commented: “Integrating BioNeMo into Sapio’s AI-driven research platform gives scientists access to advanced generative AI models for drug discovery. With AlphaFold2, MoIMIM, and DiffDock NIMs, researchers can predict, optimize, and validate drug candidates with greater speed and accuracy. This work underscores AI’s growing role in transforming pharmaceutical research and accelerating the path to breakthrough treatments.”

Integrating NVIDIA technologies into the Sapio Platform is one of the many ways Sapio Sciences is helping to improve and accelerate biopharma discovery, clinical diagnostics, and drug manufacturing.

Google plans to release new ‘open’ AI models for drug discovery

2025-03-18 - 2025-03

During a health-focused event in New York on Tuesday, Google announced that it’s developing a collection of “open” AI models for drug discovery called TxGemma.

The AI models, which Google said will be released through its Health AI Developer Foundations program later this month, can understand both “regular text” and the structures of different “therapeutic entities,” including chemicals, molecules, and proteins, according to the company.

“The development of therapeutic drugs from concept to approved use is a long and expensive process, so we’re working with the wider research community to find new ways to make this development more efficient,” Karen DeSalvo, chief health officer at Google, wrote in a blog post provided to TechCrunch. “[R]esearchers can ask TxGemma questions to help predict important properties of potential new therapies, like how safe or effective they might be.”

Google didn’t say whether the models’ license will allow for commercial use, customization, or fine-tuning. TechCrunch reached out to the company for more information and will update if the company responds.

Countless companies, including Google spin-out Isomorphic Labs, have promised that AI could one day revolutionize drug discovery by dramatically accelerating the earliest R&D steps. While there have been some successes, AI has not provided an immediate magical solution in the lab.

Several firms employing AI for drug discovery, including Exscientia and BenevolentAI, have suffered high-profile clinical trial failures in recent years. Meanwhile, the accuracy of leading AI systems for drug discovery, like Google DeepMind’s AlphaFold 3, tends to vary widely.

Still, big pharma — and investors — appear to be enthusiastic about the tech’s potential. In January, Isomorphic, which has partnerships with pharma giants Eli Lilly and Novartis, said that it expects testing on its AI-designed drugs to begin sometime this year. By one estimate, more than 460 AI startups are working on drug discovery, and investors have poured $60 billion into the space so far.

Discovery of new protein could revolutionize cancer drug delivery

2025-03-17 - 2025-03

A previously unknown protein in a family of bacteria found in soil and the human gut microbiome has been discovered – which could help drug delivery in cancer treatment.

In a paper published in PNAS, researchers at King's College London and the University of Washington describe the unique 3D structure of this protein, which is now being used to develop cancer drug delivery systems that can target drugs to tumour sites.

The bacterial protein identified by the team, which they have named BeeR, has a similar function to actin – the most abundant protein in the majority of human cells. In the presence of a chemical called ATP, actin molecules can join together into long spiral chains known as filaments. The filaments sit in the outer membrane of cells and have many important functions, including helping cells to maintain their shape, divide and move. Actin can also break down ATP, which triggers the filaments to disassemble.

Bacteria have similar proteins to actin that form filaments in the presence of ATP and help to control cell shape and division. But when studying BeeR, the researchers discovered one striking difference to other actin-like bacterial proteins – its structure.

Dr. Julien Bergeron, Senior Lecturer in the Randall Centre for Cell & Molecular Biophysics at King's College London, who led the research, said: "We used metagenomics data – extensive sequencing of bacterial genomes from the environment – to identify a previously unknown actin-like protein in a family of bacteria known as Verrucomicrobiota.

"Using the most advanced cryo-electron microscopes, we were able to determine the atomic structure of this protein, demonstrating that instead of a filament, it forms a rigid tube, with a hollow cavity at its centre. This is strikingly different from actin, or any of its other bacterial counterparts."

Dr. Bergeron first discovered the protein when he was a postdoctoral researcher at the University of Washington School of Medicine in Seattle in the lab of Professor Justin Kollman, senior author of the paper. But, at the time, they were unable to solve the structure of the protein.

After moving to King's and studying the protein using advanced imaging techniques, Dr. Bergeron – with the help of lab group members Shamar Lale-Farjat, Hanna Lewicka and Chloe Parry – found that in the presence of ATP, BeeR assembles into three stands that form a hollow, tubular structure.

Through his spin-out company Prosemble, Dr Bergeron is now working to exploit the unique structure of the hollow BeeR tubes to create protein nanoparticles for the targeted delivery of anticancer drugs to tumour sites. The team is currently testing the approach in pre-clinical breast cancer models.

"Not only are the BeeR structures tubular, but they also have a cavity at their centre that is big enough to contain drug molecules. Since we can easily control the assembly and disassembly of the tube with ATP, it gives us a simple method to deliver and release the drugs at the desired location," says Dr. Bergeron.

The work was funded by the Biotechnology and Biological Sciences Research Council, Human Frontier Science Program and the National Institute of General Medical Sciences of the National Institutes of Health.

Boehringer Ingelheim and Salipro enter drug discovery partnership

2025-03-14 - 2025-03

Boehringer Ingelheim and Sweden-based Salipro Biotech have entered a research and licence agreement to expedite the development of several drug targets.

The partnership is set to progress the discovery and development of new therapeutic solutions that target G protein-coupled receptors (GPCRs), transporters and ion channels, which are crucial in cardio-renal-metabolic and mental health diseases.

Salipro platform technology will be leveraged in the partnership. This platform stabilises membrane proteins in their native states, facilitating their usage in drug discovery programmes.

It brings together Salipro Biotech’s expertise and its platform technology for stabilising targets from the drug discovery programmes of Boehringer, enabling and expediting the development of new therapeutics.

Boehringer plans to carry out structural and biophysical studies which include cryo-electron microscopy on its pipeline targets to detect potential candidates.

Boehringer will provide Salipro with research and option payments.

Salipro is qualified for success-based milestone payments contingent on the developmental outcomes produced by Boehringer under this partnership.

Salipro Biotech CEO Jens Frauenfeld stated: “We are excited to collaborate with the innovative teams at Boehringer Ingelheim.

“By combining our Salipro platform technology with Boehringer Ingelheim’s expertise in biophysical and structural studies, we enable new possibilities in drug discovery and development.”

The Swedish company has established several research partnerships with pharmaceutical and biotech entities. It aims to expedite drug discovery via in-house and collaborated pipelines.

In December 2024, the company announced the receipt of patents granted for its Salipro platform technology in both Australia and Singapore.

It also facilitates drug discovery programmes, which include screening small molecule drugs, structure-based drug design and antibody discovery.

In October 2023, Salipro Biotech entered a multi-target antibody research agreement with Icosagen, a biotech company.

The evolution of AI in drug discovery: learning from history’s mistakes

2025-03-14 - 2025-03

AI is transforming drug discovery, but its adoption mirrors past technological shifts in the industry. In this first part of a two-part series, we reveal Sujeegar Jeevanandam’s observations of the parallels between AI and the electronic lab notebook revolution, highlighting key challenges, lessons learned, and what the future holds for machine learning in pharmaceutical research.

Sujeegar Jeevanandam, a veteran with 13 years of experience in life sciences R&D solutions, shares key insights into the evolving role of artificial intelligence in drug discovery. Drawing from his extensive work with pharmaceutical and biotechnology companies across North America, Sujeegar offers a unique perspective on how AI adoption might mirror previous technological shifts in the industry.

Historical parallels: the electronic lab notebook revolution
Reflecting on the industry fervour for AI, Sujeegar draws an illuminating parallel between today’s AI revolution and the electronic lab notebook (ELN) transformation of the early 2000s. During the ELN transition, organisations eagerly adopted various platforms, often implementing multiple solutions simultaneously without proper governance. This initial enthusiasm led to challenges: organisations found themselves managing numerous systems, struggling with licensing costs and searching for specialised developers to maintain these platforms.

The comparison is particularly pertinent, revealing a pattern in how the life sciences industry adopts new technologies. An initial period of intense enthusiasm and rapid adoption is later followed by challenges that necessitate a more strategic approach. Sujeegar suggests that AI adoption is following a similar trajectory, with organisations currently in the early, eager phase of adoption.

The future of AI in drug discovery
Looking ahead five to ten years, Sujeegar envisions machine learning models becoming just as integral to research and development as ELNs are today. However, he emphasises that this integration has not happened yet – machine learning models are not yet part of scientists’ routine workflows. Instead, they remain at the experimentation stage, with scientists interested in their potential but not yet fully incorporating them into daily operations.

A significant transformation Sujeegar anticipates is the shift from wet lab to dry lab experiments. He predicts that in the future, scientists will conduct extensive decision-making processes using AI models at their desks before entering the laboratory. However, he clarifies that wet labs won’t disappear entirely. Instead, their role will evolve to focus on validating simulation results rather than being the primary experimental platform. This represents a fundamental shift in the drug discovery paradigm, where in silico experiments could take projects almost to the endpoint, with wet lab validation serving as the final confirmation step.

Accelerating Drug Discovery and Development with Effective Data Housekeeping

2025-03-12 - 2025-03

Every household seems to have that drawer of miscellaneous belongings. When you need something, you know it’s in there, but it still takes hours to sift through the clutter you should’ve tossed years ago. The massive amount of data being stored in ever-expanding biobanks is a bit like ‘that drawer,’ and it makes finding consistently good quality data increasingly challenging.

Living in the digital age, there’s no shortage of information available, and organizations of all sorts are making it their business to use it. This is especially true now, with advanced technology allowing businesses to analyze vast swathes of data in a way that, up until recently, simply wasn’t possible.

Over the past few decades, healthcare authorities worldwide have established large biobanks, including patient medical records, clinical trial data, and genetic information. While these biobanks hold immense value, the data quality is often inconsistent and comes in a variety of shapes and formats, making it difficult to draw reliable comparisons. Trying to analyze them side by side is like comparing apples to oranges – it just doesn’t work.

This inconsistency is becoming a significant issue. It not only hampers collaboration but also limits the potential of powerful tools like AI and machine learning to deliver meaningful insights. That’s why it’s important to develop a strategy for managing health data globally to streamline the drug development process and, in turn, create more effective outcomes for patients.

The Era of ‘Big Data’ is Now
There’s no denying that data offers immense opportunities to transform a range of industries, especially healthcare. However, if data quality is poor, it can hinder how effectively the data can be used. According to a study by Sun et al. (2022)1, up to 50% of clinical trials fail due to a lack of clinical efficacy. Put simply, if the initial insights aren’t based on strong data-driven evidence, then the treatment is unlikely to be optimally effective.

As advances in technology, such as AI, continue to enhance our ability to extract insights from data, the drive to collect it at an unprecedented pace – and the potential to revolutionize drug development – has never been greater. However, the technology is only as powerful as the data it processes; without high-quality, well-curated data, even the most advanced AI models can falter.

Inconsistent formats, varying degrees of accuracy, and incomplete datasets are common challenges that, without proper attention, can undermine the effectiveness of data-driven insights2. Disparities like these can result in spurious conclusions, ineffective treatments, and missed opportunities to address patients’ unmet needs.

Making Data FAIR
To fully maximize the potential of advanced techniques in healthcare – such as AI, machine learning, and other analytical methods – meticulous data curation and management are essential3. Clean, comprehensive, and well-structured data provides the solid foundation upon which these advanced techniques can build transformative innovations in the healthcare industry.

Govt floats EOI for doing major Research & Development in pharma medtech sector

2025-03-10 - 2025-03

New Delhi: The government has sought expressions of interest (EoI) for research & development (R&D) projects to develop novel technologies in medicine, including medical devices and drugs for communicable and non-communicable diseases.

The focus will be on drug discovery for hypertension, lung disease, cancer, diabetes and rare diseases. The EoIs have been floated by the department of pharmaceuticals, under the ministry of chemical and fertilizers for its scheme called Promotion of Research and Innovation in Pharma MedTech Sector (PRIP).

PRIP aims to transform India into a global powerhouse for R&D in the pharma and MedTech sector, with a total financial outlay of ₹5,000 crore – made up of two components.

The first component, with an outlay of ₹700 crore, aims to set up Centres of Excellence (CoEs) at seven National Institutes of Pharmaceutical Education and Research (NIPERs). This will focus on anti-viral and anti- bacterial drug discovery and development, medical devices, bulk drugs, phyto-pharmaceuticals, biological therapeutics etc. The government has already set up CoEs at all the NIPERs and work has already begun.

The second component with ₹4,250 crore aims to accelerate investments in the R&D ecosystem.

Under this component, the government will finance companies focused on doing R&D in priority areas, including drug discovery & development, medical devices, stem cell therapy, and treatment of drug-resistant patients.

Amit Agrawal, Secretary, Department of Pharmaceuticals said, “All funds will go the industry alone, but industry can also come along in partnership with academia. The government will financial assistance to the firms to promote R&D. India has a strong hold in complex generics and biosimilars segment; however, the country needs to have a great focus in areas like new biological and chemical, entity, organ drugs, precision medicine, drug development for anti-microbial resistance.”

Currently Indian constitutes 3.4% market share of the global pharma industry. If the industry adopts a business-as-usual approach, the market value would grow to around $108 billion by 2030 at 11% CAGR, according to the government.

The Indian pharma industry has largely remained confined to generic drugs where it holds global leadership. But the total amount spent on pharma R&D in India is only around $3-5 billion per year, compared with $50-60 billion in the US and $15-20 billion in China.

As planned under the scheme, the bigger companies can seek funding of upto ₹125 crore while startups can secure funding of ₹1 crore, over a period of five years, based on their milestones.

British scientists unveil world-leading technology to speed up drug discovery

2025-03-10 - 2025-03

A new 3D bio-printer that produces human-like tissue holds the potential to revolutionize the discovery of new drugs for cancer, heart disease and arthritis.

Scientists from Newcastle University, with funding from Versus Arthritis, have developed a unique approach to bioprinting cell-filled gels that can closely mimic human tissues, earning it a patent in the US and Europe.

While bioprinting has been around for several years, it has had multiple limitations that the team, led by Professor Kenny Dalgarno, has overcome. Such is the promise of the discovery, they have set up a spin out company, Jetbio, to attract investment to roll out the ReJI printer for use in labs across the world.

The groundbreaking process, called Reactive Jet Impingement (ReJI) bioprinting technology, jets two different liquids at each other, one containing cells suspended in a cross-linking solution and the other a polymer solution. These mix in mid-air to form a cell filled hydrogel that can be printed on almost any surface. The printing method increases cell density to around ten times that of other bioprinting technology and at a significantly faster rate too. The resulting tissues are much closer models to human tissue samples.

Lucy Donaldson, director of research at Versus Arthritis which funded the research through the Tissue Engineering and Regenerative Therapies Centre Versus Arthritis, said: “The JetBio team are in the vanguard of research driving forward new technologies that promise to improve both the quality and speed of drug development. These advances can potentially bring new drugs to the population sooner – and that applies to treatments for arthritis, cancer and cardiovascular disease. This is a very exciting leap forward.”

A key stage of the drug development process is testing on in-vitro cell cultures which have traditionally involved growing cells on top of a flat surface, like a microscope slide. In vitro means a process or test performed outside the body in artificial conditions, such as a slide or in a test tube. However these 2D models aren’t comparable to the human body where cells interact with other cells in a 3D environment.

Bringing Jetbio’s technology to labs to print cells in a 3D matrix, more closely mimicking human tissues, can make tests more accurate thereby revolutionizing what is currently a protracted process with high rates of failure, explained Professor Dalgarno.

The landscape of drug discovery is changing and interest in new technology is growing. With the potential for the British start up to tap into a rapidly expanding global market, the Jetbio team were invited to showcase the technology to ministers and senior figures in public health, including Chief Medical Officer for England Professor Sir Chris Whitty, at the Houses of Parliament earlier this month. “There was a real buzz around our technology and the potential it holds” said Professor Dalgarno of the event, who is confident its discovery could have a far-reaching impact that could be felt in real terms across the globe. He added: “Drug development is very expensive. Therefore if you can do it more effectively and cheaper then it democratizes the process to a degree, as more people could then afford the drugs that did make it to market.”

Insilico Medicine's AI-driven drug Rentosertib receives official generic name

2025-03-07 - 2025-03

Insilico Medicine("Insilico"), a clinical stage generative artificial intelligence (AI)-driven biotechnology company, recently announced that its breakthrough drug candidate for idiopathic pulmonary fibrosis (IPF) – Rentosertib (formerly known as ISM001-055) – has been granted an official generic name by the United States Adopted Names (USAN) Council. Rentosertib stands as the first investigational drug in which both the biological target and the therapeutic compound were discovered using generative AI.

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, scary lung disease characterized by a progressive and irreversible decline in lung function. Affecting approximately 5 million people worldwide, IPF carries poor prognosis, with a median survival of 3 to 4 years. Current treatments, including antifibrotic drugs, can slow disease progression but do not stop or reverse it, leaving a significant unmet need for more effective, disease-modifying therapies.

Insilico adopted a pioneering approach to anti-IPF research by leveraging its proprietary Pharma.AI platform to discover innovative therapeutics with the potential to halt or even reverse disease progression. The process began with PandaOmics, the platform's biology engine, which analyzed vast omics and clinical datasets to identify TNIK (TRAF2 and NCK-interacting kinase) as a promising novel target for IPF. Building on this discovery, researchers used Chemistry42, the platform's generative chemistry engine, to swiftly design and optimize new small-molecule compounds targeting TNIK, leading to the nomination of Rentosertib as the preclinical candidate.

This integrated AI-driven workflow drastically accelerated the development timeline, progressing from initial target identification to a preclinical candidate in just 18 months, as detailed in Insilico's Nature Biotechnology paper published in March 2024. Notably, the name Rentosertib is partially derived from Feng Ren, PhD, Insilico's co-CEO and Chief Scientific Officer, who was also the first author of the landmark publication.

"I am honored to have witnessed and led Rentosertib from target discovery to clinic development," said Feng Ren, PhD, co-CEO and Chief Scientific Officer of Insilico Medicine, " An official generic name is typically assigned as a drug enters mid-stage development, signifying recognition of its potential as a new therapy. Rentosertib now joins the list of recognized drug candidates and will be referred to by this name in scientific literature and future clinical trials, replacing its laboratory code. We aim to rapidly advance the clinic development of this program, providing innovative options for patients while bringing solid validation for the AI-driven drug discovery industry."

Currently, Rentosertib has successfully advanced through multiple clinical studies with encouraging results. In two Phase I trials conducted in New Zealand and China, Rentosertib was administered orally to healthy subjects in Phase I trials, yielding consistent results. The studies demonstrated favorable safety, tolerability, and pharmacokinetics (PK) profiles of Rentosertib, providing robust evidence to support its progression to Phase II clinical trials.

UTSA chemist to advance drug discovery using light to activate catalysts

2025-03-06 - 2025-03

A UTSA chemist is pioneering a novel approach to drug discovery by using catalysts that are triggered by light. This innovative approach, paired with advanced automated lab techniques, could lead to the next generation of antibiotics.

Christopher Sandford, assistant professor of chemistry in the UTSA College of Sciences, is using $1.8 million in National Institutes of Health (NIH) funding for the project, entitled, “Switchable Organometallic Catalyst for Organic Reaction Development.” His work focuses on using light to activate metal-based catalysts, which could lead to faster and more effective drug development.

Antibiotic resistance is a nationally growing crisis. According to the U.S. Centers for Disease Control and Prevention, over 2.8 million antimicrobial-resistant infections occur in the United States each year, with more than 35,000 people dying as a result.

“This is an opportunity to design and build a new generation of antibiotics,” Sandford said. “Our goal is to develop new technologies that can improve the selectivity afforded in the automated synthesis of libraries of molecules that can be used in multiple areas of medicine.”

Chemists often use an advanced laboratory technique called automated parallel synthesis, which allows them to create and test many different variations of a molecule at once. However, one challenge in using this technique is being able to precisely control these chemical reactions to ensure modifications occur only within targeted areas without impacting the rest of the molecule.

Sandford’s research is addressing this challenge by developing a specialized catalyst that responds to different wavelengths of visible and ultraviolet light. This would allow researchers to trigger specific chemical transformations at the molecular level with unprecedented precision, which would streamline the process of drug discovery.

Beyond the impact in developing new medicines, the project will provide career-readiness opportunities for UTSA students. Undergraduate and graduate students will receive hands-on training and learn advanced laboratory techniques that will prepare them for careers in research and drug discovery.

IDEAYA Biosciences, Inc. (IDYA) Partners with ATTMOS for AI-Driven Drug Discovery Platform

2025-03-06 - 2025-03

China and the US have been at a crossroads over the past year over access to advanced chips and semiconductor equipment as technological rivalry heats up. While the US has been the most aggressive, it might have to rethink its export controls amid reports that Chinese buyers have found a way around them. Therefore, gaining access to its advanced artificial intelligence chips.

In order to limit China’s access to semiconductors for training and powering cutting-edge artificial intelligence, Washington has enforced export controls since 2022. President Joe Biden’s administration issued several directives and export controls that sought to stop China from acquiring the most cutting-edge chips. In the interest of national security, it also prevented China from obtaining sophisticated semiconductor equipment.

However, according to the Wall Street Journal, a clandestine network of brokers has emerged and is increasingly circumventing the restrictions. The report indicates that Chinese buyers might have the upper hand on routing advanced chips through third parties. The report further indicates that six AI servers with restricted Hopper chips have been delivered to at least two Chinese universities in Shenzhen and Wuhan since December.

Singapore’s government has already charged three men in a case that involves the sale of semiconductors that may violate US export laws. “We will not tolerate individuals and companies violating our laws or taking advantage of their association with Singapore to circumvent export controls of other countries,” Home Affairs Minister K Shanmugam told a press briefing.

As it stands, the new administration under President Donald Trump faces an uphill task of finding ways to curb access to advanced technologies through third parties as the technological rivalry heats up. In a research note to investors, analysts at Jefferies reiterated that the US could tighten chip export controls, a development that could escalate the US-China tech war.

The analysts said that Trump has specifically instructed them to use export controls to close any gaps in existing regulations and guarantee and improve the U.S. technological edge. According to Jefferies, this sets the stage for the United States to impose more stringent export regulations on China.

The push for stringent and aggressive restrictions comes as China changes its approach from employing symbolic legal and regulatory changes to investing in advanced technologies. According to an analysis at the Emerging Technology Observatory (ETO), China is currently producing the majority of the fundamental research that may support future computer hardware. If those efforts become commercially viable, the US may soon be unable to use export restrictions to maintain its competitive edge in the design and manufacturing of high-performance microchips.

By doing this, Beijing is indicating that it has taken the time to assess its own economic advantages, disadvantages, and vulnerability to US sanctions and is now more assured of its ability to compete on an equal footing with the US. There are some distinctive features to China’s strategy, such as the establishment of an economic early-warning system, even though some of it can be viewed as mimicking US export restrictions against Chinese tech firms.

For this article, we selected AI stocks by going through news articles, stock analysis, and press releases. These stocks are also popular among hedge funds in Q4 2024.

Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds.

ALS disrupts sleep, but an insomnia medication may help

2025-03-05 - 2025-03

Clinicians and researchers once thought that the deadly neurodegenerative disease amyotrophic lateral sclerosis (ALS) was very selective, in that it killed motor neurons in the brain and spinal cord but left other neurons untouched. “This is completely wrong,” said Luc Dupuis, a neuroscientist at the University of Strasbourg. “The rest of the brain is actually affected earlier.”

Many people with ALS start to experience weight loss up to 15 years before their motor symptoms manifest, and in 2023 Dupuis tied this change to the loss of certain neurons in the hypothalamus, which regulates both essential sensations like hunger and functions like sleep (1). Dupuis grew curious if changes in the hypothalamus might be responsible for the poor sleep quality that researchers have observed in ALS (2).

In a recent Science Translational Medicine study, Dupuis and his team showed that people with a recent ALS diagnosis as well as younger, seemingly asymptomatic people who carried mutations that cause familial ALS had altered sleep cycles (3). Administering sleep hormone therapeutics to mice with ALS-like symptoms helped to normalize the animals’ sleep, suggesting that these medications could alter disease progression or at least improve quality of life for people with ALS.

As people with ALS lose the neurons that control their diaphragm, they struggle to breathe, and shallow breathing can cause them to wake up frequently in the night. “But this is secondary,” said Dupuis. “The question we wanted to ask was whether there could be sleep problems long before the disease.”

That’s why he and his team chose to study people with ALS who had yet to experience respiratory symptoms and asymptomatic younger relatives of patients who had ALS caused by inheritable genetic mutations. The scientists measured the brain activity of sleeping participants via electroencephalography (EEG). Even though they did not have any complaints about their sleep, the researchers found that both groups spent more of their sleep cycle in a state of wakefulness and less time in deep sleep. These changes even occurred in younger relatives in their forties, who were likely 20 years away from manifesting symptoms.

“It’s a really good first step,” said Shyuan Ngo, a neuroscientist at the University of Queensland who was not involved in the study. She hopes that Dupuis’ team will track the study participants over time to see if and how sleep alterations relate to the onset and severity of symptoms.

In the meantime, Dupuis wanted to explore whether interventions that target sleep could be worth pursuing for people with ALS. To do so, he turned to mouse models of the disease. First, his team used EEG and found that mice with three different mutations that cause ALS-like symptoms experienced increased wakefulness and less time in deep sleep, like humans.

To find out whether these sleep disturbances could be treated, Dupuis and his team gave two of their mouse models melanin-concentrating hormone (MCH), which promotes sleep and releases from the hypothalamic neurons that Dupuis had previously found depleted in people with ALS.

The results were mixed. Both groups of mice experienced less wakefulness and spent more time dreaming, but only one spent more time in deep sleep. In fact, MCH exacerbated the deep sleep deficit for the second group of mice.

Nevertheless, the mutation in the second group of mice causes such rapid loss of motor neurons and paralysis that Dupuis and his team were able to check how early MCH treatment affected late-stage disease outcomes for those animals. Although the mice did not survive longer than their untreated counterparts, they retained more of the neurons in their lower spinal cord later into the course of their disease.

Part of the issue may have been that Dupuis’ team delivered MCH continuously. “It did not respect at all the circadian rhythm,” he said. Consequently, the mice were always a bit drowsy, not well-rested.

Dupuis’ team then gave all three groups of mice the insomnia medication suvorexant, which inhibits the wakefulness hormone orexin, at dawn when the nocturnal rodents naturally become sleepy. Although he had never observed a loss of orexin-releasing neurons in people with ALS, he figured that a drop in MCH levels might coincide with a rise in orexin levels because of the two hormones’ antagonistic relationship. “It’s a tug-of-war between MCH and orexin neurons to regulate sleep,” said Dupuis.

Sure enough, suvorexant normalized the sleep cycle in each mouse model of ALS. Now that Dupuis and his team have evidence of the medication’s immediate impact, they want to see what happens if they give it to the animals over longer periods of time. Dupuis wants to move quickly to see if this pill, which the FDA has already approved for insomnia, could help people with ALS.

Both Dupuis and Ngo consider ALS to be less of a clearly defined disease and more of a syndrome with environmental and genetic causes that vary from person to person. Ngo said that even though there is not currently evidence that poor sleep plays a causal role in the disease or its progression, better sleep quality would improve quality of life for everyone, including people with ALS.

IDEAYA Biosciences Announces Collaboration with ATTMOS to Accelerate AI/ML-Enabled Drug Discovery for Unprecedented First-in-Class Oncology Targets

2025-03-03 - 2025-03

oncology company committed to the discovery and development of targeted therapeutics, today announced a research collaboration with ATTMOS as part of its efforts to build a physics-based computational small molecule discovery platform that rapidly unlocks what are currently perceived as undruggable oncology targets.

The collaboration will integrate IDEAYA's differentiated and proven capabilities in structural biology and pharmaceutical drug discovery across multiple first-in-class oncology targets with ATTMOS's capabilities in computational chemistry method development, high performance computing, and software development. The focus of the partnership will be to engineer and optimize a workflow solution for high-throughput absolute binding free energy perturbation predictions (ABFEP) of first-in-class drug candidate molecules. This approach enables application of gold-standard physics-based statistical mechanics calculations of protein-ligand affinities at the scale required for virtual screens and represents what could become the industry's go-to standard for high-speed and high probability-of-success drug hit-finding against structurally-enabled novel biological targets.

"Current AI/ML-enabled drug discovery approaches have been largely applied to either already drugged targets or well-understood biological target classes and often fail when applied to first-in-class target opportunities. IDEAYA continues to enhance its computational drug discovery capabilities to pursue first-in-class oncology targets that are perceived as undruggable," said Michael White, Ph.D., Chief Scientific Officer, IDEAYA Biosciences. "Our partnership with ATTMOS will enable us to apply the principles of engineering to the field of drug discovery, at scale, for efficient prosecution of unprecedented oncology targets," said Paul Barsanti, Ph.D., Chief Technology Officer, IDEAYA Biosciences.

The collaboration will leverage the Amber molecular dynamics suite as the GPU-accelerated back-end free energy simulation engine. IDEAYA will train and evaluate ABFEP-based active learning cycles based on extensive ground-truth data sets derived from its successful wet-lab drug discovery campaigns against novel targets. These models will be used to screen enormous libraries of synthetically tractable chemical space for accurate and efficient de novo discovery of small molecule ligands for new targets. The work aims to overcome the limitations of current virtual screening approaches and accelerate the discovery of novel small molecule oncology therapeutics that address unmet clinical need.

About IDEAYA Biosciences

IDEAYA is a precision medicine oncology company committed to the discovery and development of targeted therapeutics for patient populations selected using molecular diagnostics. IDEAYA's approach integrates capabilities in identifying and validating translational biomarkers with drug discovery to select patient populations most likely to benefit from its targeted therapies. IDEAYA is applying its research and drug discovery capabilities to synthetic lethality – which represents an emerging class of precision medicine targets.

About ATTMOS
ATTMOS was founded in early 2022 by a diverse team of academics from Michigan State, Rutgers and UC San Diego. ATTMOS's mission is to bring novel technologies into the drug discovery process through a synergy between industry and academia and make this technology affordable and available to everybody.

How AI Is Transforming The Pharmaceutical Industry

2025-03-02 - 2025-03

The pharmaceutical industry, known for its rigorous research, complex drug development pipelines, and use of cutting-edge technology, is undergoing a transformation thanks to AI. From applications such as disease identification and diagnosis, drug discovery, optimization of clinical trials, or improving manufacturing efficiency, AI is accelerating the industry. With vast amounts of biological, chemical, and patient data at its disposal, the pharma industry is uniquely positioned to harness AI’s full potential. Because, after all, data is the heart of AI and the pharma industry has lots of data at it’s disposal.

But how exactly is AI reshaping pharma, and what does this mean for the future of drug development and medicine?

Accelerating Innovation and Reducing Drug Development Costs

Traditional drug development is a slow, expensive process. It is not uncommon for this process to take over a decade and billions of dollars to bring a new drug and treatment to market. However, now with the help of AI, pharmaceutical companies are dramatically shortening this timeline and can help drive down costs. AI tools are able to rapidly analyze large datasets of chemical compounds, biological interactions, and disease mechanisms.

Machine learning models can predict how different compounds will interact with specific biological targets, streamlining the screening process and reducing the need for many costly and time-consuming lab experiments. Since AI is great at looking at large amounts of data quickly and identifying patterns in that data, AI can also uncover hidden patterns in genomic data. These discoveries enable researchers to design drugs that precisely target diseases at a molecular level.

Beyond new drug discovery, AI is also accelerating drug repurposing. Oftentimes, there are drugs that are already developed and out on the market that can be found to effectively treat other diseases or conditions. There are many examples of this. Aspirin, originally developed as a pain reliever and anti-inflammatory drug, was later found to have blood-thinning properties. It is now widely used to reduce the risk of heart attacks and strokes for at-risk individuals. Ozempic, originally approved for type 2 diabetes management, has been repurposed to also be a weight loss drug.

Sometimes, these discoveries have been happy accidents. But now AI tools can help these discovered be more purpose driven. AI is able to analyze existing medications and see if they can be used for new treatments. By analyzing biological pathways and disease progressions, AI can match approved drugs with emerging health threats. This AI-enhanced approach helps reduce development risks, find patterns that might not have otherwise been spotted, and allow treatments to reach patients more quickly.

AI’s role in medical breakthroughs: How GPUs are transforming genomics and drug discovery

2025-03-01 - 2025-03

Impact of AI on genomics Genomics is one of the most significant areas where AI and GPUs are making a difference. The human genome consists of approximately three billion base pairs, and sequencing this data traditionally took weeks or even months. However, with the use of GPUs, the process has been drastically accelerated. “Just for one whole genome sequence, we are looking at 180 gigabytes of data,” Tripathi noted. “Using traditional approaches, generating variants from this data could take weeks or months. With GPUs, we’ve managed to generate more variants at 25 times the speed," he said. Beyond speed, AI has also helped in cost reduction. Conventional methods required nearly $35,000 per genome analysis, but AI-driven models running on GPUs have lowered the cost to under $2,000, making high-quality genomic research more accessible.

Drug discovery and AI AI’s impact extends beyond genomics into drug discovery, where it is streamlining the identification of potential new treatments. Tripathi shared how AI-assisted research has been instrumental in analysing the effectiveness of Alzheimer’s drugs. “We analysed data from 1,500 blood samples collected from 500 patients at different time points. The AI-driven insights helped validate the efficacy of the drug and demonstrated that patients with certain genomic profiles responded better to the treatment,” he explained. Another breakthrough has been in the study of protein-protein interactions, an essential aspect of understanding disease mechanisms. “Using AlphaFold3 and GPUs, we predicted 126 million protein-protein docking interactions within just four days. Traditional methods would have taken nearly ten months,” Tripathi stated.

These advancements have significant implications for personalised medicine. By understanding how specific genetic profiles interact with treatments, doctors can tailor therapies to individual patients, improving outcomes and reducing trial-and-error prescriptions. One of the most promising AI-driven studies by InnoPlexus involved pancreatic cancer, a disease notorious for its rapid progression and high mortality rate. Tripathi cited the case of a preclinical drug, ETC-569, being developed in collaboration with Singapore’s drug discovery and development center EDDC. “Pancreatic cancer is often a death sentence, with patients surviving only a few weeks post-diagnosis. Using AI, we identified potential applications of this drug in treating pancreatic and colorectal cancers,” he said.

The future of AI in healthcare As AI continues to impact healthcare, the focus is shifting from merely using AI to actively building AI solutions. Communities like the GPU Developer Community, initiated by InnoPlexus, are fostering innovation by enabling experts to collaborate on developing next-generation AI models for healthcare. “AI is not just about using ChatGPT. It’s about solving real-world problems that impact lives,” Tripathi concluded. With AI-driven breakthroughs in genomics, drug discovery, and precision medicine, the healthcare industry stands at the brink of a new era—one where disease treatment is faster, more accurate, and accessible to all.

Renovaro and BioSymetrics merge to boost AI-driven drug discovery

2025-02-27 - 2025-02

LOS ANGELES - Renovaro Biosciences Inc. (NASDAQ: RENB), a $149 million market cap company specializing in next-generation diagnostics and cancer therapies, has announced a definitive agreement to merge with BioSymetrics, an artificial intelligence (AI) firm focused on drug discovery and biomarker identification. According to InvestingPro data, Renovaro's stock has shown significant volatility, declining over 64% in the past year while experiencing a recent 10% weekly decline. The merger, expected to close in March 2025, aims to enhance Renovaro's capabilities in biomarker discovery and accelerate the development of precision medicine solutions for cancer and other diseases. This strategic move comes as the company faces financial challenges, with InvestingPro analysis indicating weak financial health scores and short-term liquidity concerns.

BioSymetrics brings to the partnership its Elion platform, an AI and machine learning engine that aids in the discovery of diagnostics and therapeutics. The platform's Phenograph serves as a translational engine, mapping clinical signals to therapeutic targets, which could expedite the identification process and enable patient stratification and drug repurposing.

The collaboration is set to integrate BioSymetrics' AI technology into Renovaro's workflow, streamlining the translation of biomarker insights into discovery timelines and enhancing research efficiency. David Weinstein, CEO of Renovaro, highlighted the merger's potential to improve the identification of new therapeutic targets and accelerate drug development.

BioSymetrics' CEO, Anthony Iacovone, also expressed enthusiasm for the merger, emphasizing the impact of the Elion platform on precision medicine and the potential for accelerated clinical applications through the partnership with Renovaro.

The combined company plans to focus on integrating AI-powered biomarker discovery with drug development, with the goal of delivering more precise and effective treatments to patients globally.

The transaction is subject to customary closing conditions and regulatory approvals. This strategic move underscores the shared commitment of both companies to harness AI and data-driven approaches to improve patient outcomes. Trading at $1.09, between its 52-week range of $0.40 to $3.38, Renovaro appears undervalued according to InvestingPro Fair Value analysis. Subscribers can access 5 additional ProTips and comprehensive financial metrics to better evaluate this emerging biotech opportunity.

This news is based on a press release statement.

In other recent news, Renovaro Inc. has secured $15 million in additional equity funding aimed at advancing its AI-driven cancer diagnostics and therapies. This financial boost is expected to expedite the company's efforts in early diagnosis and the development of precise treatments. Renovaro also announced the appointment of Nathen Fuentes as its new Chief Financial Officer, effective January 2025. Fuentes brings extensive experience from various leadership roles in the biotechnology and healthcare sectors.

Additionally, Maurice van Tilburg has been named CEO of Renovaro's subsidiary, GEDi Cube B.V., which specializes in AI-powered cancer diagnostics. Van Tilburg's appointment is anticipated to enhance the company's commercialization efforts, particularly in partnerships for early cancer detection. Renovaro has regained compliance with Nasdaq's minimum bid price rule, ensuring its continued listing on The Nasdaq Stock Market. This compliance update follows a previous notification regarding the company's stock price falling below required levels.

The company remains focused on leveraging its AI and biotechnology platforms to improve precision and personalized medicine. Renovaro's recent developments reflect its ongoing strategy to establish a stronger presence in the AI-powered healthcare solutions market.

Is AI the new magic bullet in drug research? Making progress is complex

2025-02-24 - 2025-02

Is AI the new magic bullet in drug research? Making progress is complex

Artificial intelligence is heralded as the next big leap for drug development, offering the potential to reduce costs, speed up discovery and deliver targeted, more effective treatments.

The hype surrounding machine intelligence has fueled massive investments, with billions of dollars pouring into AI-powered biotech startups.

In China alone, more than 100 AI-driven pharmaceutical companies have emerged since 2018, signaling the country's rapidly expanding role in this innovative space.

Companies like XtalPi, Deep Intelligent Pharma and StoneWise are among the most promising contenders leading the AI revolution in drug discovery.

Despite the buzz, the reality of applying AI to the complex world of drug development is not as straightforward as some would hope.

While AI has made strides in the early stages of drug discovery, its role in later, more intricate phases such as clinical trials remains fraught with challenges.

Can AI become the silver bullet for drug development? Perhaps. But the journey to revolutionize the industry will likely take years, if not decades.

The allure: speed, precision and cost-cutting

The promise of AI lies primarily in its ability to process massive datasets at remarkable speed.

Drug discovery traditionally relies on researchers sifting through vast amounts of data to identify promising molecular structures, predict drug interactions and design optimal treatment strategies.

AI can accelerate these processes by analyzing data faster and more accurately than human researchers, dramatically reducing the time it takes to develop a drug.

US-based Insilico Medicine and UK-based Exscientia, which was bought out by US-based Recursion Pharmaceuticals last year, have already made substantial progress in AI-assisted biotechnology.

Insilico's AI-designed drug for idiopathic pulmonary fibrosis advanced to human trials in less than three years an achievement that would have been considered impossible a decade ago.

This represents a significant shift in how we think about drug development timelines. AI can turn what was once a lengthy, resource-draining process into something much more rapid and cost-efficient.

AI's ability to cut costs also presents a transformative opportunity for the pharmaceutical industry. The drug development process is notorious for its high costs, with billions of dollars invested before a single candidate reaches clinical trials. AI can streamline this process by identifying viable candidates faster and more accurately, ultimately reducing the financial burden on drug developers and health-care systems alike.

In China, the AI-driven pharmaceutical ecosystem has exploded, with startups and established companies rushing to leverage machine learning to enhance their research and development processes.

Although Chinese firms are still playing catch-up to global leaders in terms of technological maturity, their rapid pace of growth and progress could close the gap in ways that are difficult to predict.

AI hype: a reality check

Despite its potential, AI faces several critical challenges that must be addressed before it can fully revolutionize drug development.

One of the most significant challenges is regulatory approval. The pharmaceutical industry is among the most heavily regulated sectors globally, and for good reason. Ensuring the safety and efficacy of new drugs requires rigorous testing. This process typically takes years, and AI-driven drug development is no exception.

AI has shown promise in streamlining the clinical trial process by predicting success rates, optimizing trial designs and even helping to recruit test patients. AI can sift through large datasets to identify ideal candidates and predict which patients are most likely to benefit from a particular treatment. While this is undoubtedly valuable, it introduces its own set of challenges.

One major issue is the quality and completeness of data.

AI is only as effective as the data it is trained on. In drug development, high-quality, comprehensive data can be scarce. Missing, biased or unrepresentative data can skew results, which is particularly problematic when conducting trials in diverse populations, such as China, where health-care data is not always as robust as in Western countries.

Inaccurate or incomplete data can undermine the results of clinical trials, delaying the approval process or, worse, leading to the approval of ineffective or unsafe treatments.

Additionally, the lack of transparency in AI decision-making remains a major challenge. AI models particularly those based on deep learning are often described as "black boxes," meaning that even their creators cannot always explain why the system makes certain decisions.

In high-stakes industries like pharmaceuticals, where patient safety is on the line, this lack of interpretability can be a significant barrier to trust. If an AI system chooses one drug candidate over another, it is crucial for researchers and regulators to understand why.

There are also significant concerns about privacy and data security.

As AI becomes more integral to the analysis of sensitive health information, there is an increasing need for safeguards to protect patient data. Ethical concerns, such as patient consent and algorithmic bias, are also top of mind.

Who owns the data used to train AI models? How are patients' rights and privacy protected? These are questions that regulators will have to grapple with as AI becomes more embedded in drug development.

The future of AI in drug development

AI has already transformed aspects of drug discovery, but it is unlikely to replace traditional methods anytime soon. Instead, the real promise of AI lies in its ability to augment human expertise. AI can accelerate some stages of the process, but it cannot solve the fundamental challenges of drug discovery on its own.

Iktos Secures Prestigious EIC Accelerator Grant to Advance its Automated AI and Robotics Platform for Drug Discovery

2025-02-21 - 2025-02

PARIS, Feb. 20, 2025 /PRNewswire/ -- Iktos, a global leader in AI-driven drug discovery, has secured a prestigious €2.5 million grant from the European Innovation Council (EIC) Accelerator to advance its AI and robotics synthesis automation technology. This funding, with the potential for an additional €5 million from the EIC, will propel Iktos' mission to revolutionize drug design by fully integrating AI-driven molecular generation, automated synthesis, and high-precision biological testing — paving the way for faster, more efficient drug discovery.

Over the next two years, the company will expand its drug discovery platform following the 2024 acquisition of Synsight and its proprietary technology, MTBench, which enables high-precision biological screening of challenging targets in biologically relevant environments.

The investment will boost Ikto's capabilities by integrating AI-driven molecular design, robotic synthesis, and biological testing. The platform is unique in that each stage of the DMTA (design-make-test-analyze) cycle is orchestrated by AI, dynamically integrating real experimental data into the generative AI at every iteration. By continuously refining predictions with real-world feedback, the cycle converges faster, drastically reducing the time needed to reach effective drug candidates. This recognition from the EIC Accelerator is a testament to Iktos' pioneering work in AI for drug discovery.

"We are honored to receive this support from the European Innovation Council," said Yann Gaston-Mathé, CEO, Iktos. "This funding allows us to further develop our AI and robotics platform to critically accelerate the discovery of new treatment options, bringing innovative therapies to patients faster and offering new hope for better health outcomes. It also underscores Europe's commitment to fostering groundbreaking AI innovations that align with ethical and strategic priorities."

Iktos' platform aligns with the EIC Accelerator Challenge, "Human-Centric Generative AI Made in Europe," advancing AI in drug discovery while prioritizing transparency, strategic autonomy, and ethical standards. Iktos enables researchers to focus on strategic decisions by enhancing human expertise while AI optimizes molecular designs and robotics execute real-world synthesis. The platform ensures AI-generated molecules are theoretically promising and synthetically viable, leveraging virtual synthesis and high-quality datasets validated by expert oversight. This approach strengthens the reliability of AI-driven outputs and supports more efficient drug development.

About Iktos
Iktos is a global leader in artificial intelligence and robotics for drug discovery. Its generative AI technology designs molecules in silico, optimized to meet all key success criteria of a discovery project. Available as SaaS software and through strategic collaborations with pharmaceutical companies, Iktos combines AI-driven molecular design with robotic synthesis and biological testing, accelerating drug discovery. With over 60 successful projects to date, Iktos is also advancing its own pipeline of drug candidates in oncology, obesity and metabolism, inflammatory and autoimmune diseases. Iktos completed a €15.5M Series A financing round, co-led by M Ventures and Debiopharm Innovation, with participation from Omnes Capital. For more information, visit Iktos.ai.

Contact

Webinar on Drug Discovery

Coming soon...

tomjamieson77@gmail.com

info@globalmeetx.net

+44-7723-860457

Free energy methods and digital transformation of drug discovery

Drug uptake discovery could allow IV medications to be taken orally

The Future of Drug Discovery: Integrating Human, AI and Robotics

Drug Discovery Outsourcing Market Estimated to Surpass $7.5 Billion by 2031, Growing at 7.8% CAGR | TMR Analysis

Elix and PRISM BioLab Join Forces to Accelerate AI-Driven Drug Discovery for Challenging Protein-Protein Interaction Targets

GraphBAN: Making drug discovery faster and more affordable through artificial intelligence

AI in Drug Discovery: A Game-Changer for the Pharmaceutical Industry

Meet STITCHR, the retrotransposon-based gene editor that enables large, scarless DNA insertions

Engineering bacteriophages to produce proteins in the gut

High-Throughput Screening Techniques To Improve Cellular Drug Discovery

Quantum Computing Meets AI: D-Wave Quantum (QBTS)’s Breakthrough in Drug Discovery

Advancements in hit identification for membrane protein drug discovery

OICR funds five innovative cancer drug discovery projects

Mount Sinai Launches AI Small Molecule Drug Discovery Center

Google DeepMind’s drug discovery spin-off Isomorphic Labs raises $600mn

New drug OLX-07010 shows promise in treating tau diseases

Predictive Oncology (POAI) Uses AI to Slash Drug Discovery Timelines & Boost Cancer Research

Overcoming delivery hurdles, IL-12 tackles ovarian cancer

Scientists at Newcastle University create a bioprinting method to speed up drug discovery

New light-driven process could accelerate the discovery of new drugs

Google releases new AI models for drug discovery

NVIDIA GTC: AI Innovation Driving Change in Healthcare and Drug Discovery

Sapio Sciences makes AI-Native drug discovery seamless with NVIDIA BioNeMo

Google plans to release new ‘open’ AI models for drug discovery

Discovery of new protein could revolutionize cancer drug delivery

Boehringer Ingelheim and Salipro enter drug discovery partnership

The evolution of AI in drug discovery: learning from history’s mistakes

Accelerating Drug Discovery and Development with Effective Data Housekeeping

Govt floats EOI for doing major Research & Development in pharma medtech sector

British scientists unveil world-leading technology to speed up drug discovery

Insilico Medicine's AI-driven drug Rentosertib receives official generic name

UTSA chemist to advance drug discovery using light to activate catalysts

IDEAYA Biosciences, Inc. (IDYA) Partners with ATTMOS for AI-Driven Drug Discovery Platform

ALS disrupts sleep, but an insomnia medication may help

IDEAYA Biosciences Announces Collaboration with ATTMOS to Accelerate AI/ML-Enabled Drug Discovery for Unprecedented First-in-Class Oncology Targets

How AI Is Transforming The Pharmaceutical Industry

AI’s role in medical breakthroughs: How GPUs are transforming genomics and drug discovery

Renovaro and BioSymetrics merge to boost AI-driven drug discovery

Is AI the new magic bullet in drug research? Making progress is complex

Iktos Secures Prestigious EIC Accelerator Grant to Advance its Automated AI and Robotics Platform for Drug Discovery