Learning Neural Generative Dynamics for Molecular Conformation GenerationDownload PDF

Sep 28, 2020 (edited Feb 10, 2022)ICLR 2021 PosterReaders: Everyone
  • Keywords: Molecular conformation generation, deep generative models, continuous normalizing flow, energy-based models
  • Abstract: 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.
  • One-sentence Summary: A novel probabilistic framework to generate valid and diverse molecular conformations. Reaching state-of-the-art results on conformation generation and inter-atomic distance modeling.
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  • Code: [![github](/images/github_icon.svg) luost26/CGCF-ConfGen](https://github.com/luost26/CGCF-ConfGen) + [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](https://paperswithcode.com/paper/?openreview=pAbm1qfheGk)
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