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.

分子表示学习具有在药物发现和酶设计等众多生物化学应用中的显著影响。我们提出了第一个全面评估从同构体集合学习的潜力的 MoleculAR Conformer Ensemble Learning (MARCEL) 基准，并建议有前景的研究方向。我们的研究结果表明，直接从可访问的同构体空间学习可以提高各种任务和模型的性能。