Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Title:Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Volume: 11 Issue: 1

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

Affiliation:

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

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.

Cite this article as:

Cano Gaspar, García-Rodríguez José and Pérez-Sánchez Horacio, Improvement of Virtual Screening Predictions using Computational Intelligence Methods, Letters in Drug Design & Discovery 2014; 11 (1) . https://dx.doi.org/10.2174/15701808113109990054