Publisher： Bentham Science Publishers
E-ISSN： 1875-5402|3|6|525-533
ISSN： 1386-2073
Source： Combinatorial Chemistry & High Throughput Screening, Vol.3, Iss.6, 2000-12, pp. : 525-533
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Abstract
The performance of artificial neural network (ANN) models in predicting pharmacological classification of structurally diverse drugs based on their theoretical chemical parameters was demonstrated. The classification coefficients for psychotropic agents, b-adrenolytic drugs, histamine H 1 receptor antagonists and drugs binding to a-adrenoceptors were 100, 100, 95 and 86 percent, respectively. A set of easily accessible non-empirical molecular parameters describing the structure of xenobiotics can provide information allowing the prediction of some pharmacological properties of drugs and drug candidates employing ANN models. Since ANN analysis can help cluster as well as segregate drugs and drug candidates according to their known and expected pharmacological properties, the number of routine biological assays might be reduced. The results presented here might be used to improve the efficiency of high throughput screening programs for new drug hits by demonstrating a promising procedure for diverse combinatorial library design and evaluation.
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