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Virtual Screening for COX-2 Inhibitors with Random Forest Algorithm and Feature Selection

Published: 08 December 2017 Publication History

Abstract

The virtual screening technology has been widely used in the drug development process to shorten the development cycle with the help of quantitative structure-activity relationship (QSAR) modelling and machine learning. When constructing the training set for machine learning model, the redundancy of molecular descriptors can seriously affect the accuracy of the established learning model. In this paper, we propose to use the F-score based feature selection to select appropriate subset of molecular descriptors as the training set, and then employ the random forest algorithm to establish the classification model for predicting potential cyclooxygenase-2 (COX-2) inhibitors. The results demonstrate that our proposed method can improve the prediction accuracy of virtual screening for COX-2 inhibitors than without feature selection, and it also shows better prediction performance compared with SVM (Support Vector Machine) based classification model.

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Cited By

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  • (2020)Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculationsMolecular Simulation10.1080/08927022.2020.176455246:11(812-822)Online publication date: 18-May-2020

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  1. Virtual Screening for COX-2 Inhibitors with Random Forest Algorithm and Feature Selection

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    cover image ACM Other conferences
    ICBRA '17: Proceedings of the 4th International Conference on Bioinformatics Research and Applications
    December 2017
    91 pages
    ISBN:9781450353823
    DOI:10.1145/3175587
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 December 2017

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    Author Tags

    1. COX-2 inhibitor
    2. Drug discovery
    3. Machine learning
    4. QSAR
    5. Random forest
    6. Virtual screening

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    • (2020)Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculationsMolecular Simulation10.1080/08927022.2020.176455246:11(812-822)Online publication date: 18-May-2020

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