From Data Factory to Better Drugs: Part 6 - Exploring Novel Chemical Space

"Our integrated system narrows millions of molecules down to a short list that's synthetically accessible. Not just promising in silico, but actually makeable before a single one is sent to the labs." Traditional drug design often leans on decades of precedent and familiar chemistry. In Part 6 of our platform series, SVP of Drug Design Donovan Chin, Ph.D., explains how Recursion breaks that mold by getting a full computational design cycle up and running in about a day. Our AI-native engine vastly increases the aperture for our chemists. Instead of relying solely on known binding pockets, they can efficiently explore entirely novel and complex chemical space. By leveraging our integrated data, the team can forecast non-obvious design hypotheses and predict how to synthesize molecules even when nothing like them currently exists. This process begins by scoring binding affinity using advanced 3D structural models like Nesso-1. From there, the team runs hundreds of integrated models covering ADME, potency, advanced simulations, and AI retrosynthesis. This unified system rapidly filters millions of potential molecules down to a precise shortlist of high-quality candidates that are physically ready for the lab. 🚀 Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #DrugDesign #ComputationalChemistry #DrugDiscovery #MachineLearning