用等变流匹配生成更准的分子构象，速度快且模型轻。
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
- 基于等变流匹配，直接在原子坐标上生成构象。
- 生成构象精度和物理合理性显著提升，推理速度更快。
- 适合需要高效生成高质量分子结构的研究者。
给定分子图预测低能分子构象是计算药物发现中的重要但具挑战性的任务。现有最先进方法要么依赖大规模基于Transformer的模型，在构象空间中扩散，要么使用计算成本高昂的方法生成初始结构并在扭转角空间中扩散。本文提出等变变压器流（ET-Flow）。我们证明，通过设计合理的流匹配方法并引入等变性与谐波先验，可避免复杂的内部几何计算和庞大的模型架构，与当前主流方法相反。该方法直接作用于全原子坐标，假设极少，具有简洁性和可扩展性。得益于等变性和流匹配优势，ET-Flow显著提升了生成构象的精度与物理有效性，同时模型更轻、推理更快。代码已公开：https://github.com/shenoynikhil/ETFlow。
原文摘要 · Abstract (English)
Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles. In this work, we introduce Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed flow matching approach with equivariance and harmonic prior alleviates the need for complex internal geometry calculations and large architectures, contrary to the prevailing methods in the field. Our approach results in a straightforward and scalable method that directly operates on all-atom coordinates with minimal assumptions. With the advantages of equivariance and flow matching, ET-Flow significantly increases the precision and physical validity of the generated conformers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow.
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