Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

We
herewith present a novel approach to predict protein–ligand
binding modes from the single two-dimensional structure of the ligand.
Known protein–ligand X-ray structures were converted into binary
bit strings encoding protein–ligand interactions. An artificial
neural network was then set up to first learn and then predict protein–ligand
interaction fingerprints from simple ligand descriptors. Specific
models were constructed for three targets (CDK2, p38-α, HSP90-α)
and 146 ligands for which protein–ligand X-ray structures are
available. These models were able to predict protein–ligand
interaction fingerprints and to discriminate important features from
minor interactions. Predicted interaction fingerprints were successfully
used as descriptors to discriminate true ligands from decoys by virtual
screening. In some but not all cases, the predicted interaction fingerprints
furthermore enable to efficiently rerank cross-docking poses and prioritize
the best possible docking solutions.