2016/12/08 by Matthew Ragoza, Joshua Hochuli, Elisa Idrobo +2 · 1 citation
Mathematics · Computer Science · Biochemistry, Genetics and Molecular Biology · #stat.ML #cs.LG #q-bio.BM
paper · pdf · doi:10.1021/acs.jcim.6b00740
arxiv created 2016/12/08 · arxiv updated 2020/10/19
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 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 non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.