Development of a virtual screening method for identification of "frequent hitters" in compound libraries.
A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.
Roche
Olivier
O
F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland.
Schneider
Petra
P
Zuegge
Jochen
J
Guba
Wolfgang
W
Kansy
Manfred
M
Alanine
Alexander
A
Bleicher
Konrad
K
Danel
Franck
F
Gutknecht
Eva-Maria
EM
Rogers-Evans
Mark
M
Neidhart
Werner
W
Stalder
Henri
H
Dillon
Michael
M
Sjögren
Eric
E
Fotouhi
Nader
N
Gillespie
Paul
P
Goodnow
Robert
R
Harris
William
W
Jones
Phil
P
Taniguchi
Mikio
M
Tsujii
Shinji
S
von der Saal
Wolfgang
W
Zimmermann
Gerd
G
Schneider
Gisbert
G
D016208:Databases, Factual; D016014:Linear Models; D015394:Molecular Structure; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009930:Organic Chemicals / Q000737:chemistry*; D004364:Pharmaceutical Preparations / Q000737:chemistry
D009930:Organic Chemicals; D004364:Pharmaceutical Preparations