Improvement of Virtual Screening Predictions using Computational Intelligence Methods
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.
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Document Type: Research Article
Publication date: January 1, 2014
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- Letters in Drug Design & Discovery publishes original letters on all areas of rational drug design and discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, and structure-activity relationships. The emphasis will be on publishing quality papers very rapidly. Letters will be processed rapidly by taking full advantage of Internet technology for both the submission and review of manuscripts. The journal is essential reading to all pharmaceutical scientists involved in research in drug design and discovery.
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