Regularized Molecular Conformation Fields

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

Keywords: random fields, conformation generation, molecular fragmentation

Abstract:

Predicting energetically favorable 3-dimensional conformations of organic molecules from
molecular 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 sampling
from 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.

TL;DR: RMCF uses a Markov Random Field to learn the joint probability distribution of fragment configurations and dihedral angles to sample from different low-energy regions of a conformation space.