Nesso-1：加速开源结合亲和力预测
原标题：Nesso-1: Accelerating Open-Source Binding Affinity Predictions
AI 摘要
Valence 和 Recursion 团队发布了 Nesso-1，一个开源粗粒度共折叠模型，用于蛋白质-配体结合亲和力预测。相比 Boltz-2，Nesso-1 速度快一个数量级以上，在多个公共和专有数据集上达到或超越其准确性，且单 GPU 预测仅需 1 秒。该模型旨在解决封闭源码瓶颈、计算成本高、人类与 AI 归纳偏差以及公共基准泄漏等问题，并支持大规模化合物筛选。
正文节选
Depending on the stage of the drug-discovery pipeline, our constraints change drastically. During the initial phase of Hit-Identification (Hit-ID), we are looking for any hit – a compound that shows the desired activity. This is a broad search across a vast chemical space (often the Enamine REAL space) to collect diverse starting points that can later be refined into potent compounds with desirable pharmacological properties. Speed here is the main axis: if a screening tool takes too long to rej