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Leash Bio发布Minos智能体科研推理沙盒

AI 总结

Latent Space播客节目介绍了Leash Bio的Ian Quigley,该公司利用DNA编码文库(DEL)采集高通量蛋白质—小分子相互作用数据。节目称,这些数据可提供数万条“lab-in-the-loop”模拟,用于智能体的科学强化学习,并已发展成Minos;Leash Bio同日发布完整预印本、博客和开放数据集。节目还提到,仅用自家数据训练的Hermes在公开基准上紧跟Boltz,而其Kaggle竞赛中约2000支队伍在未见化学库上均未超过随机水平。上述表述均来自播客节目介绍,预印本与数据集本身尚未在材料中呈现。

深度见解

据Latent Space介绍,Leash Bio认为其多年采集的DEL数据可转化为数万条实验室闭环模拟,供智能体进行可验证的科学推理强化学习。

A few years ago I stumbled upon Leash Bio’s Substack, and was hooked. Be it their Kaggle competition that showed that no, we really haven’t solved protein-ligand binding, or their crazy discovery that knowing a molecule’s author is perhaps all you need to know to predict how good it is. Their substack — or at least Owl Posting’s1 deep dive on them — should be required reading for any scientist trying to do chemistry with AI.

So I was super excited to finally be able to sit down with Ian Quigley from Leash Bio, and pick his brain. Leash specializes in DNA Encoded Libraries (aka “DELs”), a way of collecting ridiculously high-throughput interaction data between proteins and small molecules.2 Before I discovered Leash, I had heard nothing but horror stories from DEL users. And yet, Ian and friends somehow found a way to tame the beast, extracting clean and reproducible data that is just perfect for large scale modeling. Without teasing too much, the answer comes down to epistemic rigor, and an incredible conviction that you just have to Do The Thing.

#Leash Bio#Minos#Hermes#Boltz#ChEMBL#Recursion#DNA-encoded libraries

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