The Future of AI in Drug Discovery with Tom Neyarapally
What’s next for AI in drug discovery and development? Tom Neyarapally, CEO of Archetype Therapeutics, shares his outlook on how converging technologies—from LLMs to spatial omics—are reshaping how we discover and deliver drugs. He emphasizes that while AI tools are evolving rapidly, the integration of diverse data modalities and collaborative innovation between nimble startups and large pharma is essential. Neyarapally also touches on reducing late-stage trial failures and costs—key challenges that AI is finally beginning to address. For stakeholders focused on AI in drug development, this conversation offers both strategic insight and practical optimism.
Episode Contents:
About the Guest
Tom Neyarapally is CEO and Co-Founder of Archetype Therapeutics, an AI-native biotech company pioneering generative chemogenomics for patient-centered drug discovery and repurposing.
Key Takeaways
- AI is accelerating the speed and specificity of drug discovery.
- Integrating multiple technologies is key to real clinical progress.
- Smaller companies play a crucial role in innovation alongside big pharma.
- Reducing trial failures can drastically cut drug development costs.
- Broader access to diverse, high-quality datasets is the next frontier.
Condensed Transcript
What excites you most about the future of AI in drug discovery?
The pace is incredible. New algorithms and AI tools now evolve in months, not years. What took us five years in network biology now gets leapfrogged in three months with LLMs and new AI tools. But what's most exciting is convergence—how different innovations like target discovery, delivery mechanisms, and phenotypic screening come together. The next few years will be about integrating these elements to deliver real clinical impact.
How can we make drug development more efficient and cost-effective?
Today’s process is filled with inefficiencies—physical high-throughput screening, animal testing with minimal human data input, and costly trial failures. With AI and better patient data, we can virtualize large parts of this workflow. We can screen billions of molecules per day, loop real-world data into model refinement, and ultimately lower development costs and failure rates.
What’s the biggest opportunity no one is working on?
Universal access to high-quality, multi-modal datasets. AI toolkits are abundant, but they’re often constrained by lack of data. Companies aggregating niche datasets (e.g., single-cell data) are promising, but we need broader, systemic efforts to unlock and connect datasets across modalities for AI to reach its full potential in drug discovery.
About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.