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Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble

by Olutomilayo Olayemi Petinrin1, Faisal Saeed2, Xiangtao Li1, Fahad Ghabban2, Ka-Chun Wong1,3,*

1 Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
2 Information Systems Department, College of Computer Science and Engineering, Taibah University, Tayba, Medina, 42353, Saudi Arabia
3 Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR

* Corresponding Author: Ka-Chun Wong. Email: email

(This article belongs to the Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 70(3), 4745-4762. https://doi.org/10.32604/cmc.2022.020523

Abstract

Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets, and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R2 of 0.9423. The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation. The ensemble model has also shown robust performance in its yield estimation ability and efficiency.

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Cite This Article

APA Style
Petinrin, O.O., Saeed, F., Li, X., Ghabban, F., Wong, K. (2022). Reactions’ descriptors selection and yield estimation using metaheuristic algorithms and voting ensemble. Computers, Materials & Continua, 70(3), 4745-4762. https://doi.org/10.32604/cmc.2022.020523
Vancouver Style
Petinrin OO, Saeed F, Li X, Ghabban F, Wong K. Reactions’ descriptors selection and yield estimation using metaheuristic algorithms and voting ensemble. Comput Mater Contin. 2022;70(3):4745-4762 https://doi.org/10.32604/cmc.2022.020523
IEEE Style
O. O. Petinrin, F. Saeed, X. Li, F. Ghabban, and K. Wong, “Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble,” Comput. Mater. Contin., vol. 70, no. 3, pp. 4745-4762, 2022. https://doi.org/10.32604/cmc.2022.020523



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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