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Bangalore, India

Jayaraj P.B.,National Institute of Technology Calicut | Ajay M.K.,National Institute of Technology Calicut | Nufail M.,National Institute of Technology Calicut | Gopakumar G.,National Institute of Technology Calicut | Jaleel U.C.A.,Center for Cheminformatics
Journal of Cheminformatics | Year: 2016

Background: In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Results: Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Conclusion: Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases. © 2016 Jayaraj et al.

Sajeev R.,Center for Cheminformatics | Athira R.S.,Center for Cheminformatics | Nufail M.,Center for Cheminformatics | Jinu Raj K.R.,Center for Cheminformatics | And 4 more authors.
Journal of Computational Electronics | Year: 2013

Virtual screening methods were adopted for modeling and prediction of semi conductivity of Schiff base molecules. The predictive models built using data mining methods that were generated from descriptor based technology was able to give an alternative method to the currently used HOMO-LUMO gap based prediction methodologies. The predictions using the discriminative classifiers such as, Naïve Bayes, Random forest, Support Vector Machine and Decision tree analysis in the machine learning algorithms could predict new semi-conductor molecules. © 2013 Springer Science+Business Media New York.

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