Learning Neural Generative Dynamics for Molecular Conformation Generation

• URL: http://arxiv.org/abs/2102.10240v1
• Date: Sat, 20 Feb 2021 03:17:58 GMT
• Title: Learning Neural Generative Dynamics for Molecular Conformation Generation
• Authors: Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang
• Abstract summary: We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
• Score: 89.03173504444415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study how to generate molecule conformations (\textit{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.

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