Abstract: A system and method for structure-based, small molecule activity prediction using binding mode prediction information. Binding scores between ligands and target molecules, (e.g. proteins, RNA, DNA, lipids, sugars) are first generated using molecular docking. A first machine learned deep neural network (DNN) model is developed using data representing the molecular ligand-target pair 3D structures and docking features to predict binding modes. Using transfer learning, weights of layers learned in the first machine learned model are used as weights in layers of a second machine learned DNN model used to more accurately improve the performance of activity prediction of the second machine learned model. For a target newly paired ligand-target complex, the method further implements a binding mode selector for selecting one or more particular binding poses for input to the activity prediction model for use in activity mode prediction of an activity of the target paired ligand-protein complex.

Abstract: A computer-implemented method is described. The method includes generating, by a ligand bond graph generator, a first graph based on bond connectivity within a ligand molecule that is specified as input. The method further includes generating, by a ligand-protein graph generator, a second graph based on a contact map of the ligand molecule and a target molecule that is specified as another input. The method further includes receiving docking prediction metrics for the ligand molecule and the target molecule. The method further includes inputting, to a deep neural network, as input features, the first graph, the second graph, and the docking prediction metrics. The method further includes determining, using the deep neural network, a binding mode prediction that characterizes a set of potential interactions between the ligand molecule and the target molecule.