Development of a virtual screening method for identification of "frequent hitters" in compound libraries.
J Med Chem
; 45(1): 137-42, 2002 Jan 03.
Article
en En
| MEDLINE
| ID: mdl-11754585
ABSTRACT
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.
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Bases de datos:
MEDLINE
Asunto principal:
Compuestos Orgánicos
/
Bases de Datos Factuales
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Idioma:
En
Revista:
J Med Chem
Asunto de la revista:
QUIMICA
Año:
2002
Tipo del documento:
Article
País de afiliación:
Suiza