用几何流同步优化分子构象与化学结构，提升药物设计精度。
EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design
- 统一自然参数空间，沿费舍尔-罗水度量演化分子几何流
- 在CrossDock上达到93.4%的PoseBusters通过率，超越基线
- 适合需要高保真分子构象生成的研究者使用
基于结构的药物设计（SBDD）旨在发现具有生物活性的配体。传统方法分别在欧几里得空间和概率空间构建连续原子坐标与离散化学类别间的概率路径，导致与底层统计流形不匹配。本文提出将分子表示为复合指数族分布，使坐标与类别在统一的自然参数空间中，沿费舍尔-罗水度量下的指数测地线同步演化。为避免直接指向狄拉克分布引起的轨迹坍塌，我们提出面向SBDD的动态演化指数测地流（EvoEGF-Mol），采用渐进参数精炼架构，以动态聚焦分布替代静态狄拉克目标。模型在CrossDock数据集上达到93.4%的PoseBusters通过率，展现卓越的几何精度与相互作用保真性；在真实世界的MolGenBench任务中，于生物活性骨架恢复方面优于基线方法。代码已开源。
原文摘要 · Abstract (English)
Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifolds. We address this issue by representing molecules using composite exponential-family distributions, where coordinates and categories are represented within a unified natural parameter space to evolve synchronously along exponential geodesics under the Fisher-Rao metric. To avoid the instantaneous trajectory collapse induced by geodesics directly targeting Dirac distributions, we propose Evolving Exponential Geodesic Flow for SBDD (EvoEGF-Mol), which replaces static Dirac targets with dynamically concentrating distributions and is trained with a progressive-parameter-refinement architecture. Our model approaches a reference-level PoseBusters passing rate (93.4%) on CrossDock, demonstrating remarkable geometric precision and interaction fidelity, while achieving superior performance over baseline methods on real-world MolGenBench tasks for bioactive scaffold recovery. Code is available at https://github.com/BLEACH366/EvoEGF-Mol.
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