Issue |
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 3, June 2023
|
|
---|---|---|
Page(s) | 257 - 270 | |
DOI | https://doi.org/10.1051/wujns/2023283257 | |
Published online | 13 July 2023 |
Computer Science
CLC number: R 965.1; Q-03
Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates
School of Information Management, Nanjing University, Nanjing 210023, Jiangsu, China
Received:
30
November
2022
Breast cancer is presently one of the most common malignancies worldwide, with a higher fatality rate. In this study, a quantitative structure-activity relationship (QSAR) model of compound biological activity and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties prediction model were performed using estrogen receptor alpha (ERα) antagonist information collected from compound samples. We first utilized grey relation analysis (GRA) in conjunction with the random forest (RF) algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity, and then we used Spearman correlation analysis to identify 16 independent variables. Second, a QSAR model of the compound were developed based on BP neural network (BPNN), genetic algorithm optimized BP neural network (GA-BPNN), and support vector regression (SVR). The BPNN, the SVR, and the logistic regression (LR) models were then used to identify and predict the ADMET properties of substances, with the prediction impacts of each model compared and assessed. The results reveal that a SVR model was used in QSAR quantitative prediction, and in the classification prediction of ADMET properties: the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene) properties, the LR model predicts the cytochrome P450 enzyme 3A4 subtype (CYP3A4) and Micronucleus (MN) properties, and the BPNN model predicts the Human Oral Bioavailability (HOB) properties. Finally, information entropy theory is used to validate the rationality of variable screening, and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability, which can be used as a reference for screening probable active compounds and drug discovery.
Key words: anti-breast cancer / drug discovery / quantitative structure-activity relationship (QSAR) model / ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction / machine learning
Biography: XU Zonghuang, male, Ph.D. candidate, research direction: information science, mathematical modeling, big data analysis, machine learning and national scientific and technological intelligence. E-mail: zonghuangxu@yeah.net
Fundation item: Supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_0082)
© Wuhan University 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.