# ROCS-Derived Features for Virtual Screening

arXiv: https://arxiv.org/abs/1606.01822 · HTML: https://mlsanity.com/paper/1606.01822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Target-Free Compound Activity Prediction via Few-Shot Learning](https://mlsanity.com/paper/2311.16328/) (2023)

## Abstract (arXiv)

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.