LeashBio 访谈：湿实验变成 RL 环境，智能体一个周末跑一万次药物优化
From Wet Lab to RL Env: 10,000 Drug-Discovery Campaigns in a Weekend @LeashBio’s @allmeasures expla...
把湿实验变成智能体能刷的 RL 环境，一个周末跑一万多次药物优化，Kaggle 竞赛里没人赢过随机基线。
LeashBio 在 Latent Space 访谈中介绍，DNA-encoded libraries（DEL）技术能产生数十亿条蛋白质–分子结合测量数据。在该公司举办的 Kaggle 竞赛中，没有一个团队在未见过的化学库上跑赢随机基线，说明公开数据集容易让模型认出化学家而不是学会化学。通过把湿实验改造成可回放的 RL 环境，智能体一个周末能跑超过 10,000 次药物化学优化任务。访谈还对比了 Hermes 与 Boltz 两套工具，并提到从 DEL 命中分子推进到小鼠实验只用了六个月。
From Wet Lab to RL Env: 10,000 Drug-Discovery Campaigns in a Weekend @LeashBio’s @allmeasures expla...
From Wet Lab to RL Env: 10,000 Drug-Discovery Campaigns in a Weekend @LeashBio ’s @allmeasures explains how DNA-encoded libraries generate billions of protein–molecule measurements, why no team in Leash’s Kaggle competition beat chance on an unseen chemical library, how public datasets can teach AI to recognize the chemist instead of understand the chemistry, why engineering protein mutations exposes model memorization, and how replayable experimental worlds let agents run over 10,000 medicinal chemistry campaigns in a weekend. Plus: Hermes vs. Boltz, moving from DEL hits to mouse studies in six months, and why experimental rigor and the Bitter Lesson of scaling could unlock a new generation of AI-driven drug discovery. Your browser does not support the video tag. 🔗 View on Twitter 💬 0 🔄 2 ❤️ 4 👀 456 📊 2 ⚡
