Automated 3D Pre-Training for Molecular Property Prediction

Abstract

Molecular property prediction is an important problem in drug discovery and materials science. As geometric structures have been demonstrated necessary for molecular property prediction, 3D information has been combined with various graph learning methods to boost prediction performance. However, obtaining the geometric structure of molecules is not feasible in many real-world applications due to the high computational cost. In this work, we propose a novel 3D pre-training framework (dubbed 3D PGT), which pretrains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Based on fact that bond length, bond angle, and dihedral angle are three basic geometric descriptors corresponding to a complete molecular 3D conformer, we first develop a multi-task generative pre-train framework based on these three attributes. Next, to automatically fuse these three generative tasks, we design a surrogate metric using the total energy to search for weight distribution of the three pretext tasks since total energy corresponding to the quality of 3D conformer. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT compared to various pre-training baselines. CCS CONCEPTS• Computing methodologies → Neural networks; • Applied computing → Molecular structural biology.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

“…A crucial aspect is the inclusion of 3D geometry information. While the exploration of 3D models has predominantly focused on reactive transition states and simulation acceleration within the field of chemical reactions, 3D models pretraining frameworks are still widely employed in models for single molecules. ,− The primary challenge lies in the fact that the conformation sampling software currently struggles to sample 3D structures for reactive multiple-molecule systems. Nevertheless, inspiration can be drawn from molecule pretraining frameworks that encode 3D information even when only molecular graphs are available.…”