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.

翻译：我们研究如何从分子图中产生分子一致性(即3D结构),传统方法,例如分子动态、通过计算费用昂贵的模拟进行样本一致性等传统方法,最近,机器学习方法通过培训大量收集符合性数据,显示出巨大的潜力;由于收集符合性的复杂分布的模型能力有限,以及难以在原子之间建立远距离依赖性模型,挑战出现;由于最近深层基因化模型的进展,我们在本文件中提出了一个新的概率性框架,以根据分子图产生有效和多样化的符合性;我们提出了一种方法,将流动基模型和能源基模型的优势结合起来,享有:(1) 估计多式联运符合性分布的高模型能力;(2) 明确捕捉观察空间各原子之间复杂的长期依赖性;广泛的实验表明拟议方法在几个基准方面的优异性表现,包括相容生成和距离建模任务,大大改进了分子一致性抽样的现有基因化模型。