Nooshin Arabi, Mohammad Reza Torabi, Afshin Fassihi, Fahimeh Ghasemi
{"title":"Identification of potential vascular endothelial growth factor receptor inhibitors via tree-based learning modeling and molecular docking simulation","authors":"Nooshin Arabi, Mohammad Reza Torabi, Afshin Fassihi, Fahimeh Ghasemi","doi":"10.1002/cem.3545","DOIUrl":null,"url":null,"abstract":"<p>Angiogenesis, a crucial process in tumor growth, is widely recognized as a key factor in cancer progression. The vascular endothelial growth factor (VEGF) signaling pathway is important for its pivotal role in promoting angiogenesis. The primary objective of this study was to identify a powerful classifier for distinguishing compounds as active or inactive inhibitors of VEGF receptors. To build the machine learning model, compounds were sourced from the BindingDB database. A variety of common feature selection techniques, including both filter-based and wrapper-based methods, were applied to reduce dimensionality, subsequently, overfitting problem. Robust and accurate tree-based classifiers were employed in the classification procedure. Application of the extra-tree classifier using the MultiSURF* feature selection method provided a model with superior accuracy (83.7%) compared with other feature selection techniques. High-throughput molecular docking followed by an accurate docking and comprehensive analysis of the results was performed to provide the best possible inhibitors of these receptors. Comprehensive analysis of the docking results revealed successful prediction of molecules with VEGFR1 and VEGFR2 inhibitory activity. These results emphasized that the performance of the extra-tree model, coupled with MultiSURF* feature selection, surpassed other methods in identifying chemical compounds targeting specific VEGF receptors.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemometrics","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cem.3545","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL WORK","Score":null,"Total":0}
引用次数: 0
Abstract
Angiogenesis, a crucial process in tumor growth, is widely recognized as a key factor in cancer progression. The vascular endothelial growth factor (VEGF) signaling pathway is important for its pivotal role in promoting angiogenesis. The primary objective of this study was to identify a powerful classifier for distinguishing compounds as active or inactive inhibitors of VEGF receptors. To build the machine learning model, compounds were sourced from the BindingDB database. A variety of common feature selection techniques, including both filter-based and wrapper-based methods, were applied to reduce dimensionality, subsequently, overfitting problem. Robust and accurate tree-based classifiers were employed in the classification procedure. Application of the extra-tree classifier using the MultiSURF* feature selection method provided a model with superior accuracy (83.7%) compared with other feature selection techniques. High-throughput molecular docking followed by an accurate docking and comprehensive analysis of the results was performed to provide the best possible inhibitors of these receptors. Comprehensive analysis of the docking results revealed successful prediction of molecules with VEGFR1 and VEGFR2 inhibitory activity. These results emphasized that the performance of the extra-tree model, coupled with MultiSURF* feature selection, surpassed other methods in identifying chemical compounds targeting specific VEGF receptors.
期刊介绍:
The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.