3D Interaction Geometric Pre-training for Molecular Relational Learning

TL;DR

This paper presents 3DMRL, a novel pre-training strategy that enables molecular relational learning models to incorporate 3D geometric information without expensive quantum calculations, significantly improving performance on various tasks.

Contribution

Introduces a 3D geometric pre-training method for MRL that leverages a virtual interaction environment to learn 3D info efficiently.

Findings

Up to 24.93% performance improvement across 40 tasks.

Effective in out-of-distribution and extrapolation scenarios.

Demonstrates the feasibility of 3D pre-training without costly quantum calculations.

Abstract

Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D interaction geometry remains prohibitively expensive. This paper introduces a novel 3D geometric pre-training strategy for MRL (3DMRL) that incorporates a 3D virtual interaction environment, overcoming the limitations of costly traditional quantum mechanical calculation methods. With the constructed 3D virtual interaction environment, 3DMRL trains 2D MRL model to learn the global and local 3D geometric information of molecular interaction. Extensive experiments on various tasks using real-world datasets, including out-of-distribution and extrapolation scenarios, demonstrate the effectiveness of 3DMRL, showing up to a 24.93% improvement in performance across 40 tasks. Our code is publicly available at https://github.com/Namkyeong/3DMRL.