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Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

Publication Type : Journal Article

Publisher : Biomedical and Pharmacology Journal

Source : Biomedical and Pharmacology Journal, Volume 11, p.1513-1519 (2018)

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Keywords : Classification Algorithms, Drug likeness, Ensemble Methods, Lipinski’s Rule, Molecular Descriptors, virtual screening

Campus : Coimbatore, Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Center : Computational Bioscience

Department : Computer Science, Mathematics

Year : 2018

Abstract : In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively

Cite this Research Publication :
Ani R., Manohar, R., Anil, G., and Dr. Deepa Gopakumar O. S., “Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier”, Biomedical and Pharmacology Journal, vol. 11, pp. 1513-1519, 2018

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