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.
Original language | English (US) |
---|---|
State | Published - 2021 |
Event | 9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online Duration: May 3 2021 → May 7 2021 |
Conference
Conference | 9th International Conference on Learning Representations, ICLR 2021 |
---|---|
City | Virtual, Online |
Period | 5/3/21 → 5/7/21 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language