用Transformer流模型直接生成分子三维构象，速度快且精度高。
Conformation Generation using Transformer Flows
- 基于Transformer的流模型，直接在坐标空间采样生成构象
- 对大分子构象生成精度提升最高达40%（相比现有方法）
- 适合需要快速高精度分子构象生成的研究者使用
估算分子图的三维构象有助于理解其生物与化学功能。快速生成有效构象是分子建模的核心。近年来基于图的深度网络已将构象生成时间从小时缩短至秒级，但现有架构在大分子上难以扩展。本文提出ConfFlow，一种基于Transformer的流模型用于构象生成。与现有方法不同，ConfFlow 直接在坐标空间采样，无需施加显式物理约束。生成过程高度可解释，类似于分子动力学模拟中的力场更新。应用于大分子构象生成时，相比最先进学习方法，精度最高提升40%。源代码已开源：https://github.com/IntelLabs/ConfFlow。
原文摘要 · Abstract (English)
Estimating three-dimensional conformations of a molecular graph allows insight into the molecule's biological and chemical functions. Fast generation of valid conformations is thus central to molecular modeling. Recent advances in graph-based deep networks have accelerated conformation generation from hours to seconds. However, current network architectures do not scale well to large molecules. Here we present ConfFlow, a flow-based model for conformation generation based on transformer networks. In contrast with existing approaches, ConfFlow directly samples in the coordinate space without enforcing any explicit physical constraints. The generative procedure is highly interpretable and is akin to force field updates in molecular dynamics simulation. When applied to the generation of large molecule conformations, ConfFlow improve accuracy by up to $40\%$ relative to state-of-the-art learning-based methods. The source code is made available at https://github.com/IntelLabs/ConfFlow.
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