A Neural Network Based Virtual High Throughput Screening Test for the Prediction of CNS Activity

Authors: Keseru G.M.; Molnar L.; Greiner I.

Source: Combinatorial Chemistry & High Throughput Screening, Volume 3, Number 6, December 2000 , pp. 535-540(6)

Publisher: Bentham Science Publishers

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Abstract:

A virtual high throughput screening test to identify potentially CNS-active drugs has been developed. Discrimination was based on the knowledge available in databases containing CNS-active (Cipsline from Prous Science) and inactive compounds (Chemical Directory from Sigma-Aldrich). Molecular structures were represented using 2D Unity fingerprints and a feedforward neural network was trained to classify molecules regarding their CNS activity. The parameterized network was validated by reclassification of the training set elements, by the classification of a test set preselected from the Prous database, and also by the prediction of activity for known CNS drugs not used in the training set but available in the Medchem database (Daylight).These tests revealed that our neural net recognized at least 89% of CNS-active compounds and would be suitable for use in our virtual screening protocol.

Keywords: Neural Network; Prediction of CNS Activity; Derwent World Drug Index; Grippen atom types; Qsar techniques; BBB penetration; Medchem database; Tanimoto coefficents

Language: English

Document Type: Review article

DOI: 10.2174/1386207003331346

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