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.

翻译：分子图的三维构象估计有助于理解分子的生物与化学功能。因此，快速生成有效构象是分子建模的核心任务。基于图的深度网络最新进展已将构象生成时间从数小时缩短至数秒。然而，现有网络架构难以有效扩展至大分子体系。本文提出ConfFlow——一种基于Transformer网络的流生成模型，用于构象生成。与现有方法不同，ConfFlow直接在坐标空间进行采样，无需强制施加任何显式物理约束。该生成过程具有高度可解释性，类似于分子动力学模拟中的力场更新过程。在大分子构象生成任务中，ConfFlow相较于当前最先进的基于学习的方法，精度提升高达$40\%$。源代码已发布于https://github.com/IntelLabs/ConfFlow。