ROCS-derived features for virtual screening.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into color components and color atom overlaps, novel color similarity features that can be weighted in a system-specific manner by machine learning algorithms. In cross-validation experiments, these additional features significantly improve virtual screening performance relative to standard ROCS.
Stanford University, 318 Campus Dr. S296, Stanford, CA, 94305, USA. kearnes@stanford.edu.|Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA. kearnes@stanford.edu.
Steven
0000-0003-4579-4388
S
Kearnes
Stanford University, 318 Campus Dr. S296, Stanford, CA, 94305, USA.
Vijay
V
Pande
D000465:Algorithms; D017076:Computer-Aided Design; D015195:Drug Design; D000069550:Machine Learning; D008958:Models, Molecular; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry
D004364:Pharmaceutical Preparations
Machine learning; Structure–activity relationships; Virtual screening