  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.
