Abstract
Discovery of drugs has been a complex process, time-consuming and expensive until an alternative of making drug has been found i.e. using in silico method to discover potential inhibitor. During the process of drug design, compound classification is carried out through docking score steps. The aim of this research is to predict the docking score results using proper methods for classification i.e. a computationally based method and a standard statistical method. This research examined three target enzymes listed in DUD-E database i.e. aofb, cah2 and hs90a. Each enzyme consists of different compounds that will be classified as good inhibitor (ligand) and bad inhibitor (decoy). In this research, the docking score step is conducted by binary logistic regression and logistic regression ensemble (Lorens). Binary logistic regression yields on 90.4% of accuracy for aofb, 91.7% for cah2 and 94% for hs90a enzyme. Meanwhile, logistic regression ensemble (Lorens) results on the accuracy levels of 88.95, 92.1 and 100% for aofb, cah2 and hs90a consecutively. This paper showed that logistic regression ensemble method outperforms standard logistic regression to be used for the inhibitor classification.
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Acknowledgements
The authors gratefully acknowledge the financial support from The Ministry of Research, Technology and Higher Education Indonesia through Research Grant for International Collaboration and Scientific Publication. Moreover, the authors would like to thank also to the First EAI International Conference on Computer Science and Engineering, NOVEMBER 11–12, 2016, PENANG, MALAYSIA as well as the anonymous refrees.
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Kuswanto, H., Melasasi, J.N., Ohwada, H. (2018). Enzyme Classification on DUD-E Database Using Logistic Regression Ensemble (Lorens). In: Zelinka, I., Vasant, P., Duy, V., Dao, T. (eds) Innovative Computing, Optimization and Its Applications. Studies in Computational Intelligence, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-319-66984-7_6
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