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Open AccessArticle

Bioactive Molecule Prediction Using Extreme Gradient Boosting

1
UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia
2
Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Leif A. Eriksson
Molecules 2016, 21(8), 983; https://doi.org/10.3390/molecules21080983
Received: 1 May 2016 / Revised: 19 July 2016 / Accepted: 22 July 2016 / Published: 28 July 2016
(This article belongs to the Collection Molecular Docking)
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today’s drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound’s molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets. View Full-Text
Keywords: biological data; drug discovery; virtual screening; prediction of biological activity biological data; drug discovery; virtual screening; prediction of biological activity
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MDPI and ACS Style

Babajide Mustapha, I.; Saeed, F. Bioactive Molecule Prediction Using Extreme Gradient Boosting. Molecules 2016, 21, 983.

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