From Wet Lab to RL Env: 10,000 Drug-Discovery Campaigns in a Weekend — Ian Quigley, Leash Bio
Ian Quigley, co-founder and CEO of Leash Bio, explains how his company combines DNA-encoded libraries (DELs) and high-throughput sequencing to generate billions of reproducible protein-small-molecule binding measurements. He shares key lessons from Leash's BELKA Kaggle competition, exposes how public chemistry databases like ChEMBL cause machine learning models to learn chemist sociology rather than physical binding principles, and details how Leash built Hermes and attention-mapping techniques with engineered protein mutations. Finally, Quigley reveals how Leash converts its experimental chemical landscapes into lightning-fast reinforcement learning sandboxes for LLM reasoning agents to conduct thousands of structure-activity relationship (SAR) medicinal chemistry campaigns.
- A single researcher working in Ian Quigley's basement generated a four-billion-measurement dataset using DNA-encoded chemical libraries. Read →
- Leash Bio trained a classifier on 1,815 prolific chemists in the public ChEMBL database and hit 60% top-five accuracy predicting which scientist built a given molecule. Read →
- Sri Kosuri challenges the machine learning drug discovery model: if training a predictive network requires testing millions of physical molecules, you should simply develop the physical hits and skip the model. Read →
- Hermes paired ESM protein embeddings with ChemBERTa small molecule embeddings via cross-attention, but attention maps exposed sequence memorization rather than physical binding. Read →
- Public drug discovery databases like ChEMBL train machine learning models on human bias rather than physics because they reflect which molecules historical chemists preferred to synthesize. Read →
- Public chemistry repositories like ChEMBL train machine learning models on human publication habits rather than physical binding mechanics. Read →