If an AI drug discovery platform doesn’t produce multiple drug candidates, it’s still a demo…🔬🤷♂️
📄 In this new piece for R&D World, Brian K. Buntz interviews Alex Zhavoronkov, CEO of Insilico Medicine, in a direct and fact-driven discussion on the one thing AI drug discovery desperately needs: benchmarks 📊. Titled “Compete, don’t complain: Insilico measures AI drug discovery by benchmarks, not talk” the article offers a grounded view of what it takes to move beyond AI theory and actually build a company that delivers validated drug candidates, at speed and scale.
As an early investor in Insilico Medicine and through LongeVC, I’ve seen first-hand how the team built what many in the space only pitch: a full-stack, AI-native engine that helps turn in silico ideas into real drug candidates, quickly and repeatedly. 🚀 A few strategic aspects I think made Insilico what it is today:
✅ Tech stack as infrastructure, not a collection of tools: Insilico’s PharmaAI platform integrates target discovery (PandaOmics), molecular generation (Chemistry42), and clinical trial prediction (inClinico) into a unified pipeline, not as siloed features, but as a single operating system for drug R&D.
✅ Hardware and automation are not afterthoughts: The company’s Suzhou-based “Life Star” lab uses fully automated execution, including a bipedal humanoid robot, to physically validate AI-generated compounds at scale. It is certainly one of the most tightly integrated digital-to-wet-lab pipelines in the industry.
✅ Global structure built for speed: AI R&D in Montreal and Abu Dhabi; synthesis, preclinical, and early clinical work in Asia. Thinking strategically about geography is crucial in the modern, politically, economically, and regulatorily-complex world, and it’s how Insilico achieves operational efficiency both in R&D (as short as 9 months from target to development candidate) and business growth.
✅ Clear long-term thesis on aging: The “longevity vault” now holds 80+ dual-purpose targets that map to both aging and disease. The strategy is disciplined: get there through disease first, build clinical and regulatory momentum, and be ready when the time is right 🎯.
💭 “You don’t want to be assembling iPhones — you need to figure out how to develop the next iPhone if China is already assembling them efficiently,” Alex says, drawing a parallel between biotech innovation and how companies compete in a global system built on execution speed and scale
If you're following the intersection of AI, biotech, and translational longevity, this is a company (and article) to study closely. Link in the comments 👇