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 (ROC AUC scores) relative to standard ROCS.
Cite
@article{arxiv.1606.01822,
title = {ROCS-Derived Features for Virtual Screening},
author = {Steven Kearnes and Vijay Pande},
journal= {arXiv preprint arXiv:1606.01822},
year = {2016}
}