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

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:
(This article belongs to this 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

Received 28 May 2021; Accepted 08 July 2021; Issue published 11 October 2021

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.

Keywords

Buchwald-Hartwig reaction; descriptor selection; machine learning; metaheuristic algorithm; palladium-catalyzed cross-coupling reaction; voting ensemble

Cite This Article

O. Olayemi Petinrin, F. Saeed, X. Li, F. Ghabban and K. Wong, "Reactions’ descriptors selection and yield estimation using metaheuristic algorithms and voting ensemble," Computers, Materials & Continua, vol. 70, no.3, pp. 4745–4762, 2022.



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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