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

作　　者：Olutomilayo Olayemi Petinrin Faisal Saeed Xiangtao Li Fahad Ghabban Ka-Chun Wong 

机构地区：[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

出　　处：《Computers, Materials & Continua》2022年第3期4745-4762,共18页计算机、材料和连续体（英文）

基　　金：The work described in this paper was substantially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region[CityU 11200218];one grant from the Health and Medical Research Fund,the Food and Health Bureau,The Government of the Hong Kong Special Administrative Region[07181426];and the funding from Hong Kong Institute for Data Science(HKIDS)at City University of Hong Kong.The work described in this paper was partially supported by two grants from City University of Hong Kong(CityU 11202219,CityU 11203520);This research was substantially sponsored by the research project(Grant No.32000464)supported by the National Natural Science Foundation of China and was substantially supported by the Shenzhen Research Institute,City University of Hong Kong.The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research with the project number(442/77).

摘　　要：Bioactive compounds in plants,which can be synthesized using N-arylationmethods 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 R^(2) 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.

关 键 词：Buchwald-Hartwig reaction descriptor selection machine learning metaheuristic algorithm palladium-catalyzed cross-coupling reaction voting ensemble 

分 类 号：O62[理学—有机化学]