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Letters in Drug Design & Discovery
ISSN (Print): 1570-1808
ISSN (Online): 1875-628X
VOLUME: 11
ISSUE: 1
DOI: 10.2174/15701808113109990054









Improvement of Virtual Screening Predictions using Computational Intelligence Methods

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Author(s): Gaspar Cano, José García-Rodríguez and Horacio Pérez-Sánchez
Pages 33-39 (7)
Abstract:
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.
Keywords:
Clinical Research, Computational Intelligence, Drug Discovery, Neural Networks, Support Vector Machines, Virtual Screening.
Affiliation:
Computer Science Department, Catholic University of Murcia (UCAM) E30107 Murcia, Spain.