LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jan 15, 2019
Abstract
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.