利用模板引导分子三维构象生成，提升药物设计精度
Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
- 基于流匹配实现分子对齐，生成初始三维坐标
- 通过可微优化融合形状、药效团等多因素，精准调整构象
- 适合低相似度或高柔性分子，优于传统对接工具
预测小分子在蛋白结合位点中的三维构象是药物设计的关键挑战。当存在结晶参考配体（模板）时，可提供几何先验信息以指导三维构象预测。本文提出一种两阶段方法：第一阶段基于流匹配的分子对齐生成配体的三维坐标，以模板结构为参考；第二阶段通过可微姿态优化，基于形状与药效团相似性、内能以及可选的蛋白结合口袋信息，精炼该构象。我们构建了一个新的配体对数据集，其共结晶于同一靶点，用于评估本方法。实验表明，该方法在模板相似度低或配体柔性高的情况下，显著优于标准对接工具和开源对齐方法。
原文摘要 · Abstract (English)
Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility.
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