  The three-dimensional shape and conformation of small-molecule ligands are
critical for biomolecular recognition, yet encoding 3D geometry has not
improved ligand-based virtual screening approaches. We describe an end-to-end
deep learning approach that operates directly on small-molecule conformational
ensembles and identifies key conformational poses of small-molecules. Our
networks leverage two levels of representation learning: 1) individual
conformers are first encoded as spatial graphs using a graph neural network,
and 2) sampled conformational ensembles are represented as sets using an
attention mechanism to aggregate over individual instances. We demonstrate the
feasibility of this approach on a simple task based on bidentate coordination
of biaryl ligands, and show how attention-based pooling can elucidate key
conformational poses in tasks based on molecular geometry. This work
illustrates how set-based learning approaches may be further developed for
small molecule-based virtual screening.
