We study how to generate molecule conformations (i.e., 3D structures) from a
molecular graph. Traditional methods, such as molecular dynamics, sample
conformations via computationally expensive simulations. Recently, machine
learning methods have shown great potential by training on a large collection
of conformation data. Challenges arise from the limited model capacity for
capturing complex distributions of conformations and the difficulty in modeling
long-range dependencies between atoms. Inspired by the recent progress in deep
generative models, in this paper, we propose a novel probabilistic framework to
generate valid and diverse conformations given a molecular graph. We propose a
method combining the advantages of both flow-based and energy-based models,
enjoying: (1) a high model capacity to estimate the multimodal conformation
distribution; (2) explicitly capturing the complex long-range dependencies
between atoms in the observation space. Extensive experiments demonstrate the
superior performance of the proposed method on several benchmarks, including
conformation generation and distance modeling tasks, with a significant
improvement over existing generative models for molecular conformation
sampling.

本文提出了一种结合了流模型与能量模型的方法，以解决机器学习在从分子图生成分子空间构象时存在的挑战。经过大量实验，证明该方法在分子构象生成和距离建模任务上具有显著的改进和卓越性能。