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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Pharmacological Classification of Drugs Based on Neural Network Processing of Molecular Modeling Data

Author(s): Adam Bucinski, Antoni Nasal and Roman Kaliszan

Volume 3, Issue 6, 2000

Page: [525 - 533] Pages: 9

DOI: 10.2174/1386207003331445

Price: $65


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, β-adrenolytic drugs, histamine H1 receptor antagonists and drugs binding to α-adrenoceptors were 100, 100, 95 and 86%, 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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