  Virtual screening can accelerate drug discovery by identifying promising
candidates for experimental evaluation. Machine learning is a powerful method
for screening, as it can learn complex structure-property relationships from
experimental data and make rapid predictions over virtual libraries. Molecules
inherently exist as a three-dimensional ensemble and their biological action
typically occurs through supramolecular recognition. However, most deep
learning approaches to molecular property prediction use a 2D graph
representation as input, and in some cases a single 3D conformation. Here we
investigate how the 3D information of multiple conformers, traditionally known
as 4D information in the cheminformatics community, can improve molecular
property prediction in deep learning models. We introduce multiple deep
learning models that expand upon key architectures such as ChemProp and Schnet,
adding elements such as multiple-conformer inputs and conformer attention. We
then benchmark the performance trade-offs of these models on 2D, 3D and 4D
representations in the prediction of drug activity using a large training set
of geometrically resolved molecules. The new architectures perform
significantly better than 2D models, but their performance is often just as
strong with a single conformer as with many. We also find that 4D deep learning
models learn interpretable attention weights for each conformer.
