Improvement of Virtual Screening Predictions using Computational Intelligence Methods

ISSN: 1875-628X (Online)
ISSN: 1570-1808 (Print)

Volume 14, 12 Issues, 2017

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Letters in Drug Design & Discovery

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G. Perry
University of Texas
San Antonio, TX

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Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Letters in Drug Design & Discovery, 11(1): 33-39.

Author(s): Gaspar Cano, José García-Rodríguez and Horacio Pérez-Sánchez.

Affiliation: Computer Science Department, Catholic University of Murcia (UCAM) E30107 Murcia, Spain.


Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.


Clinical Research, Computational Intelligence, Drug Discovery, Neural Networks, Support Vector Machines, Virtual Screening.

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Article Details

Volume: 11
Issue Number: 1
First Page: 33
Last Page: 39
Page Count: 7
DOI: 10.2174/15701808113109990054

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