US 12,412,638 B2
Structure-based, ligand activity prediction using binding mode prediction information
Joseph Anthony Morrone, Pleasantville, NY (US); Jeffrey Kurt Weber, Brooklyn, NY (US); Sugato Bagchi, White Plains, NY (US); and Wendy Dawn Cornell, Warren, NJ (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corportion, Armonk, NY (US)
Filed on Feb. 3, 2021, as Appl. No. 17/166,483.
Prior Publication US 2022/0246233 A1, Aug. 4, 2022
Int. Cl. G16B 15/30 (2019.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G16B 15/30 (2019.02) [G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of predicting an activity of a ligand against a target molecule, the method comprising:
receiving, at a hardware processor, a representation of a ligand molecule and a target molecule forming a ligand-target molecule pair structure for which an activity is to be determined;
obtaining, at the hardware processor, one or more binding modes corresponding to the received ligand-target molecule pair structure;
determining, using a first neural network running at the hardware processor, a confidence metric characterizing a correctness of each of the obtained one or more binding modes, said first neural network comprising a deep neural network model trained to predict a characterizing confidence metric using labels representing a closeness of binding modes corresponding to training sets of ligand-target molecule pair structures to a reference set of binding modes;
selecting, using the hardware processor, one or more binding modes based on their corresponding characterizing metrics;
inputting, to a second neural network running at the hardware processor, as input features, the selected one or more binding modes, said second neural network comprising a deep neural network of a compatible structure as said first neural network, said second neural network trained to predict an activity based on training sets of ligand-target molecule pair structures and corresponding labels representing their known activities;
determining, using the second neural network, a prediction of an activity for said ligand-target molecule pair structure; and
outputting, by the hardware processor, the activity prediction for said ligand-target molecule pair structure, wherein said output activity prediction is formulated as a classification or a regression with improved accuracy.