Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks

Published: 28 Oct 2023, Last Modified: 28 Oct 2023NeurIPS2023-AI4Science PosterEveryoneRevisionsBibTeX
Keywords: molecular representation learning, conformer ensembles, geometric learning
TL;DR: We present the MoleculAR Conformer Ensemble Learning (MARCEL) benchmark that comprehensively evaluates the potential of learning on conformer ensembles across a diverse set of molecules, datasets, and models.
Abstract: Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on conformer ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D molecular representation learning models, along with two strategies that explicitly incorporate conformer ensembles into 3D MRL models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models.
Submission Track: Original Research
Submission Number: 204