LigVoxel: inpainting binding pockets using 3D-convolutional neural networks

Motivation Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein–ligand complexes with the intention of mimicking a chemist’s intuition at manually placing atoms when designing a new compound. We show that these models can generate spatial images of ligand chemical properties like occupancy, aromaticity and donor–acceptor matching the protein pocket. Results The predicted fields considerably overlap with those of unseen ligands bound to the target pocket. Maximization of the overlap between the predicted fields and a given ligand on the Astex diverse set recovers the original ligand crystal poses in 70 out of 85 cases within a threshold of 2 Å RMSD. We expect that these models can be used for guiding structure-based drug discovery approaches. Availability and implementation LigVoxel is available as part of the PlayMolecule.org molecular web application suite. Supplementary information Supplementary data are available at Bioinformatics online.