Identification of potential vascular endothelial growth factor receptor inhibitors via tree-based learning modeling and molecular docking simulation

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2024-04-01 DOI:10.1002/cem.3545
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,&nbsp;Mohammad Reza Torabi,&nbsp;Afshin Fassihi,&nbsp;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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过树状学习建模和分子对接模拟鉴定潜在的血管内皮生长因子受体抑制剂
血管生成是肿瘤生长的一个关键过程,被公认为是癌症进展的一个关键因素。血管内皮生长因子(VEGF)信号通路因其在促进血管生成中的关键作用而非常重要。本研究的主要目的是找出一种强大的分类器,用于区分化合物是血管内皮生长因子受体的活性抑制剂还是非活性抑制剂。为建立机器学习模型,化合物来自 BindingDB 数据库。为了降低维度和过拟合问题,研究人员采用了多种常见的特征选择技术,包括基于过滤器的方法和基于包装的方法。在分类过程中采用了稳健而准确的树型分类器。与其他特征选择技术相比,使用 MultiSURF* 特征选择方法的树外分类器提供的模型准确率更高(83.7%)。为了提供这些受体的最佳抑制剂,研究人员进行了高通量分子对接、精确对接和结果综合分析。对对接结果的综合分析表明,成功预测了具有血管内皮生长因子受体1和血管内皮生长因子受体2抑制活性的分子。这些结果表明,在鉴定针对特定血管内皮生长因子受体的化合物方面,树外模型与 MultiSURF* 特征选择相结合的性能超过了其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
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.
期刊最新文献
Issue Information Issue Information Resampling as a Robust Measure of Model Complexity in PARAFAC Models Population Power Curves in ASCA With Permutation Testing A Non‐Linear Model for Multiple Alcohol Intakes and Optimal Designs Strategies
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1