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J Chem Inf Model. 2006 Jul-Aug;46(4):1704-12.

GFscore: a general nonlinear consensus scoring function for high-throughput docking.

Author information

1
BIP Laboratory, Bioénergétique et Ingénierie des Protéines, CNRS UPR9036 Institute for Structural Biology and Microbiology (IBSM), 31 Chemin Joseph Aiguier 13402 Marseille Cedex 20, France.

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.

PMID:
16859302
DOI:
10.1021/ci0600758
[Indexed for MEDLINE]

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