Regularized Molecular Conformation Fields

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Bibtex Paper Supplemental


Lihao Wang, Yi Zhou, Yiqun Wang, Xiaoqing Zheng, Xuanjing Huang, Hao Zhou


Predicting energetically favorable 3-dimensional conformations of organic molecules frommolecular graph plays a fundamental role in computer-aided drug discovery research.However, effectively exploring the high-dimensional conformation space to identify (meta) stable conformers is anything but trivial.In this work, we introduce RMCF, a novel framework to generate a diverse set of low-energy molecular conformations through samplingfrom a regularized molecular conformation field.We develop a data-driven molecular segmentation algorithm to automatically partition each molecule into several structural building blocks to reduce the modeling degrees of freedom.Then, we employ a Markov Random Field to learn the joint probability distribution of fragment configurations and inter-fragment dihedral angles, which enables us to sample from different low-energy regions of a conformation space.Our model constantly outperforms state-of-the-art models for the conformation generation task on the GEOM-Drugs dataset.We attribute the success of RMCF to modeling in a regularized feature space and learning a global fragment configuration distribution for effective sampling.The proposed method could be generalized to deal with larger biomolecular systems.