A novel hybrid ultrafast shape descriptor method for use in virtual screening.

ABSTRACT:

Background

We have introduced a new Hybrid descriptor composed of the MACCS key descriptor encoding topological information and Ballester and Richards' Ultrafast Shape Recognition (USR) descriptor. The latter one is calculated from the moments of the distribution of the interatomic distances, and in this work we also included higher moments than in the original implementation.

Results

The performance of this Hybrid descriptor is assessed using Random Forest and a dataset of 116,476 molecules. Our dataset includes 5,245 molecules in ten classes from the 2005 World Anti-Doping Agency (WADA) dataset and 111,231 molecules from the National Cancer Institute (NCI) database. In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for trainin

SUBMITTER: Cannon EO 

PROVIDER: S-EPMC2275267 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature