Regularized Molecular Conformation Fields

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

Hall J #341
[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST
Spotlight presentation: Lightning Talks 2A-4
Tue 6 Dec 6:30 p.m. PST — 6:45 p.m. PST


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.

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