Attention-Based Learning on Molecular Ensembles

• URL: http://arxiv.org/abs/2011.12820v1
• Date: Wed, 25 Nov 2020 15:23:52 GMT
• Title: Attention-Based Learning on Molecular Ensembles
• Authors: Kangway V. Chuang, Michael J. Keiser
• Abstract summary: We describe an end-to-end deep learning approach that operates directly on small-moleculeal ensembles. We show how attention-based pooling can elucidate key conformational poses in tasks based on molecular geometry.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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