GFscore: a general nonlinear consensus scoring function for high-throughput docking. Academic Article uri icon

Overview

abstract

  • Most of the recent published works in the field of docking and scoring protein/ligand complexes have focused on ranking true positives resulting from a Virtual Library Screening (VLS) through the use of a specified or consensus linear scoring function. In this work, we present a methodology to speed up the High Throughput Screening (HTS) process, by allowing focused screens or for hitlist triaging when a prohibitively large number of hits is identified in the primary screen, where we have extended the principle of consensus scoring in a nonlinear neural network manner. This led us to introduce a nonlinear Generalist scoring Function, GFscore, which was trained to discriminate true positives from false positives in a data set of diverse chemical compounds. This original Generalist scoring Function is a combination of the five scoring functions found in the CScore package from Tripos Inc. GFscore eliminates up to 75% of molecules, with a confidence rate of 90%. The final result is a Hit Enrichment in the list of molecules to investigate during a research campaign for biological active compounds where the remaining 25% of molecules would be sent to in vitro screening experiments. GFscore is therefore a powerful tool for the biologist, saving both time and money.

publication date

  • January 1, 2006

Research

keywords

  • Molecular Structure

Identity

Scopus Document Identifier

  • 33746867935

Digital Object Identifier (DOI)

  • 10.1021/ci0600758

PubMed ID

  • 16859302

Additional Document Info

volume

  • 46

issue

  • 4