Evan Feinberg and Genesis CTO Sergey Edunov join us to talk about solving drug discovery with AI. Sergey, fresh off leading Llama 2 and Llama 3 pretraining at Meta, makes the case that the most interesting architecture work in AI right now isn't happening in language models — it's happening in 3D structure prediction, where diffusion turned out to be the missing primitive the field had been waiting for. Genesis's new model, PEARL (Place Every Atom at the Right Location), puts that to work: it doesn't just predict where a ligand binds, it models how the protein itself flexes to accommodate it. We get into why that was so hard to do until now, and why Evan thinks the field's favorite benchmark — 2Å RMSD — is mostly "slop." (Full technical report here: arxiv.org/abs/2510.24670)

We also dig into Genesis's agentic drug discovery system, SAPPHIRE, and what it actually takes for an AI agent to act like a chemist: reasoning about poses, forming hypotheses, reading literature, and proposing the next round of candidates. Plus: why finding a good drug is less "needle in a haystack" and more "hay in a needle stack," the tension between binding affinity and solubility, and how PEARL performed zero-shot on the brand-new OpenBind benchmark (genesis.ml/news/zero-shot-pearl…) against a notoriously hard induced-fit target.