title: GFscore: a general nonlinear consensus scoring function for high-throughput docking.
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.
authors.affiliation: 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.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Betzi
authors.forename: Karsten
authors.initials: K
authors.lastname: Suhre
authors.forename: Bernard
authors.initials: B
authors.lastname: Chétrit
authors.forename: Françoise
authors.initials: F
authors.lastname: Guerlesquin
authors.forename: Xavier
authors.initials: X
authors.lastname: Morelli
mesh_terms: D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship