HeyJaredDrug Discovery AI Talk
Late-breaking advances in AI-enabled drug discovery, including news, research progress, market trends, and interviews
- Indexed episodes, last 90 days
- 12
- Latest publication
- Sep 25, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 3, 2026
Latest episodes
#75. The AI Patent Landscape 2026 (opens the original)
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In this episode, we examine how AI-native biotechnology companies are reshaping drug discovery through the lens of their intellectual-property portfolios. The evidence suggests that these firms are becoming significantly more productive at generating patentable molecules, yet they have not demonstrated the same advantage in discovering entirely new biological targets. Companies such as Insilico Medicine and Genesis show clear strengths in chemical optimization and development speed, while insitr
#74. Is Drug Discovery AI Safe? (opens the original)
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In this podcast, we study how integrating agentic AI into autonomous wet labs promises rapid therapeutic innovation, while connecting autonomous models directly to physical lab instruments creates critical security risks. These range from immediate hazards—such as cyber-physical vulnerabilities, sequence-screening evasion, and flawed objective optimization—to systemic risks from unaligned superintelligence and uncontrolled biological synthesis. Mitigating these threats requires a capability-base
#73. The Triumph of Daraxonrasib (opens the original)
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For decades, KRAS stood as oncology’s archetypal “undruggable” target—a powerful cancer driver with no obvious pocket for conventional medicines to grasp. This episode explores how molecular-glue drugs such as daraxonrasib overturn that assumption by recruiting cyclophilin A to form a synthetic complex around active RAS, physically blocking its growth signals. From the structural ingenuity behind this molecular trap to emerging clinical promise in pancreatic and other KRAS-driven cancers, the da
#72. 2026 Emerging Trends (opens the original)
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In this podcast, we show a pivotal shift in 2026 for AI drug discovery toward integrated, AI-native R&D systems that move beyond simple algorithmic tasks to form closed-loop learning environments. In this new phase, the industry focuses on converting physical experiments into causal data to overcome information bottlenecks that mere model scaling cannot solve. Leading experts emphasize that generative abundance is creating a new challenge, making it more difficult to select the right candidate t
#71. Magic Wands (opens the original)
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In this episode, we critically examine whether AI drug discovery is solving the hardest problems in medicine—or simply making the easier ones faster. At the center of the discussion is Daphne Koller’s argument that the industry has invested heavily in computational molecular design while giving too little attention to the deeper challenge of understanding human disease biology. If the primary bottleneck is identifying the causal mechanisms that truly improve patient outcomes, then better molecul
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