Journal
BIOINFORMATICS
Volume 35, Issue 2, Pages 243-250Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty583
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Funding
- Acellera Ltd.
- MINECO [BIO2017-82628-P]
- FEDER
- European Union's Horizon 2020 Research and innovation programme [675451]
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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 angstrom RMSD. We expect that these models can be used for guiding structure-based drug discovery approaches.
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