Many promising drug discoveries from universities never make it to patients because researchers lack the tools and infrastructure to move from lab findings to actual drug candidates. A new partnership between Evogene, a computational chemistry company, and the Blavatnik Center for Drug Discovery (BCDD) at Tel Aviv University is designed to solve this exact problem by combining artificial intelligence with experimental validation.

Why Do Academic Drug Discoveries Fail to Progress?

Universities and academic research teams generate world-class biological insights into disease targets every year. However, translating those insights into viable drug development programs requires specialized expertise, expensive equipment, and integrated workflows that most academic labs simply do not have access to. This gap between scientific discovery and drug development has long been a bottleneck in the pharmaceutical industry, leaving promising therapies stuck in the lab.

The new initiative directly addresses this challenge by providing Israeli academic researchers and entrepreneurs with coordinated access to both computational and experimental resources. Rather than forcing researchers to navigate the drug discovery process alone, the partnership creates a unified framework that bridges the gap between biological understanding and drug candidate development.

How Does the AI-Driven Collaboration Work?

At the heart of the partnership is an integrated workflow called Design-Make-Test-Analyze (DMTA). Here is how the process unfolds:

• Design Phase: Evogene's proprietary ChemPass AI generative engine uses artificial intelligence to design and optimize novel small molecules based on the biological target identified by academic researchers.
• Make and Test Phase: The BCDD provides state-of-the-art experimental infrastructure to synthesize and validate the AI-designed molecules, screening them for efficacy and safety.
• Analyze Phase: Results feed back into the computational model, allowing the AI to learn and refine its designs iteratively, improving the likelihood of finding viable drug candidates.

This closed-loop approach is designed to reduce early-stage risk and improve development efficiency compared to traditional methods where computational design and experimental validation happen separately.

"Israel's academic institutions generate world-class discoveries and novel therapeutic targets. However, many entrepreneurs and researchers face significant hurdles in transforming these scientific insights into viable drug development programs. Through this collaboration with the Blavatnik Center for Drug Discovery, we are creating a unique framework that combines advanced AI-driven computational chemistry with world-class experimental validation capabilities," said Ofer Haviv, President and CEO of Evogene.

Ofer Haviv, President and CEO of Evogene

The partnership is particularly focused on helping researchers working on molecular glues and complex proteins, two areas where small-molecule therapeutics have shown promise but remain difficult to develop using conventional approaches.

What Does This Mean for the Broader AI Drug Discovery Landscape?

This initiative reflects a larger trend in pharmaceutical innovation: artificial intelligence is increasingly being deployed to accelerate drug discovery across multiple therapeutic areas. The cardiovascular drug development sector alone has attracted over 1.4 billion dollars in strategic investments aimed at AI-powered platforms, according to a recent market analysis.

The investments are flowing into AI applications across the entire drug development lifecycle, from target discovery and clinical trial optimization to regulatory approval and post-market safety monitoring. Major pharmaceutical companies including Pfizer, AstraZeneca, Eli Lilly, Novartis, Bayer, Johnson & Johnson, Roche, Merck, and Sanofi are actively incorporating AI tools into their research and development processes.

"The Blavatnik Center for Drug Discovery was established to advance pioneering medical research from the lab to patient treatment. Our mission has always been to bridge the gap between outstanding academic science and real-world therapeutic innovation. By partnering with Evogene, and leveraging their cutting-edge ChemPass AI engine, we can provide researchers and entrepreneurs with a comprehensive drug discovery pipeline," explained Leah Klapper, Managing Director of the BCDD.

Leah Klapper, Managing Director of the Blavatnik Center for Drug Discovery

Steps to Participate in the Initiative

For Israeli academic researchers and entrepreneurs interested in leveraging this new partnership, the process involves several key steps:

• Project Identification: Researchers with deep biological understanding of novel disease targets should contact the Blavatnik Institute to discuss their research focus and therapeutic goals.
• Evaluation and Selection: Evogene and the BCDD will evaluate promising projects emerging from Israeli academic institutions and entrepreneurial ventures to determine suitability for the collaboration.
• Integrated Development: Selected programs receive access to Evogene's ChemPass AI computational chemistry expertise for molecule design and optimization, combined with the BCDD's experimental validation and translational development support.

The collaboration is facilitated by Ramot, Tel Aviv University's technology transfer company, which helps manage intellectual property and commercialization aspects of the partnership.

Why Regulatory Agencies Are Taking Notice

Beyond academic partnerships, regulatory momentum is building around AI-driven drug development. The U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), and Japan's Pharmaceuticals and Medical Devices Agency (PMDA) are demonstrating increasing receptiveness toward real-world data and digital health tools in cardiovascular drug regulatory submissions. This regulatory acceptance creates clearer pathways for AI-enabled development and potentially accelerates time-to-market for therapies developed using these methods.

The convergence of aging global populations, rising disease burden, and technological advancement is creating compelling investment dynamics for AI-powered drug discovery. As traditional pharmaceutical development faces mounting pressure from complex patient populations and fragmented healthcare data, AI technologies are specifically addressing these structural inefficiencies in ways that conventional approaches cannot match.