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
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