SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes

2016-02-19T00:01:00Z (GMT) by David Zilian Christoph A. Sotriffer
A major shortcoming of empirical scoring functions for protein–ligand complexes is the low degree of correlation between predicted and experimental binding affinities, as frequently observed not only for large and diverse data sets but also for SAR series of individual targets. Improvements can be envisaged by developing new descriptors, employing larger training sets of higher quality, and resorting to more sophisticated regression methods. Herein, we describe the use of SFCscore descriptors to develop an improved scoring function by means of a PDBbind training set of 1005 complexes in combination with random forest for regression. This provided SFCscoreRF as a new scoring function with significantly improved performance on the PDBbind and CSAR–NRC HiQ benchmarks in comparison to previously developed SFCscore functions. A leave-cluster-out cross-validation and performance in the CSAR 2012 scoring exercise point out remaining limitations but also directions for further improvements of SFCscoreRF and empirical scoring functions in general.