IngentaConnect Pharmacological Classification of Drugs Based on Neural Network P...

Authors: Bucinski A.; Nasal A.; Kaliszan R.

Source: Combinatorial Chemistry & High Throughput Screening, Volume 3, Number 6, December 2000 , pp. 525-533(9)

Publisher: Bentham Science Publishers

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.

Keywords: Neural Network Processing; Molecular Modeling Data; Hydrophobicity Parameter; HyperChem Program; Ann model

Language: English

Document Type: Review article

DOI: 10.2174/1386207003331445

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