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 incorr

SUBMITTER: Ragoza M 

PROVIDER: S-EPMC5479431 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature