BindMol：用多目标强化学习生成多样结合模式的药物分子
Binding Mode Matters: Hotspot-Aware Drug Discovery via Explorative Preferences
做药物生成的朋友可以看看这篇：BindMol 把对接分数拆成多个 hotspot 目标来训练强化学习，比传统只优化单一分数的生成方法更能产出多样结合模式的分子，论文在 arXiv 上可读。
上海等机构研究团队提出 BindMol 框架，将基于靶点的药物分子设计重新表述为多目标探索任务，每个目标对应增强分子与某个关键 hotspot 的相互作用，相关论文已发布于 arXiv（编号 2611.14956）。框架核心是定制化的多目标强化学习算法，在训练中引入 explorative preferences，让模型不再依赖单一的标量 docking score 进行优化，从而能区分不同结合决定因素的具体贡献。实验结果显示，BindMol 能发现高亲和力、兼具结构新颖性与多样结合模式的化合物。
在靶点药物发现与多属性优化两项任务上，该方法都得到了验证。对实验 assays 而言，多样的结合模式比单一高亲和力分数更有参考价值，BindMol 提供了面向目标的药物发现新范式。
Binding Mode Matters: Hotspot-Aware Drug Discovery via Explorative Preferences
Discovering hit molecules requires not just high binding affinity, but also the identification of diverse binding modes, which are critical for experimental assays. Existing generative approaches have predominantly relied on optimizing a scalar docking score, obscuring the distinct contributions of key binding determinants. To this end, we introduce a paradigm shift by formulating target-based drug design as a multi-objective exploration task, where each objective explicitly corresponds to enhancing interactions with a specific hotspot. Here, we introduce BindMol, a novel generative framework driven by a customized multi-objective reinforcement learning algorithm. By incorporating explorative preferences during training, our approach efficiently uncovers molecules with diverse and desirable binding profiles. Empirical results demonstrate that BindMol facilitates the discovery of high-affinity compounds characterized by both structural novelty and diverse binding modes. Validated across target-based drug discovery and multi-property optimization tasks, our approach provides a versatile paradigm for goal-oriented drug discovery.
This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include BindMol, 强化学习, 药物发现, 分子生成.
Source attribution: curated from arXiv cs.LG (@Dingshuo Chen, Hao Yang, Kuangqi Zhou, Zhixun Li, Qiang Liu, Xiangxiang Zeng, Shu Wu, Yaosen Min).