Automated pharmacophore identification for large chemical data sets.
J Chem Inf Comput Sci
; 39(5): 887-96, 1999.
Article
em En
| MEDLINE
| ID: mdl-10529987
ABSTRACT
The identification of three-dimensional pharmacophores from large, heterogeneous data sets is still an unsolved problem. We developed a novel program, SCAMPI (statistical classification of activities of molecules for pharmacophore identification), for this purpose by combining a fast conformation search with recursive partitioning, a data-mining technique, which can easily handle large data sets. The pharmacophore identification process is designed to run recursively, and the conformation spaces are resampled under the constraints of the evolving pharmacophore model. This program is capable of deriving pharmacophores from a data set of 1000-2000 compounds, with thousands of conformations generated for each compound and in less than 1 day of computational time. For two test data sets, the identified pharmacophores are consistent with the known results from the literature.
Buscar no Google
Base de dados:
MEDLINE
Assunto principal:
Software
/
Desenho de Fármacos
/
Bases de Dados Factuais
Idioma:
En
Ano de publicação:
1999
Tipo de documento:
Article