title: Protein-Ligand Scoring with Convolutional Neural Networks.
abstract: Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.
authors.forename: Matthew
authors.initials: M
authors.lastname: Ragoza
authors.forename: Joshua
authors.initials: J
authors.lastname: Hochuli
authors.affiliation: Department of Computer Science, The College of New Jersey , Ewing, New Jersey 08628, United States.
authors.forename: Elisa
authors.initials: E
authors.lastname: Idrobo
authors.forename: Jocelyn
authors.initials: J
authors.lastname: Sunseri
authors.forename: David Ryan
authors.identifier: 0000-0002-6892-6614
authors.initials: DR
authors.lastname: Koes
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004353:Drug Evaluation, Preclinical; D008024:Ligands; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D014584:User-Computer Interface
chemical_list: D008024:Ligands; D011506:Proteins