Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking.

Q3 Pharmacology, Toxicology and Pharmaceutics Current drug discovery technologies Pub Date : 2022-01-01 DOI:10.2174/1570163819666220811094019

Babatunde Samuel Obadawo, Oluwatoba Emmanuel Oyeneyin, Adesoji Alani Olanrewaju, Damilohun Samuel Metibemu, Sunday Adeola Emaleku, Taoreed Olakunle Owolabi, Nureni Ipinloju

{"title":"Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking.","authors":"Babatunde Samuel Obadawo, Oluwatoba Emmanuel Oyeneyin, Adesoji Alani Olanrewaju, Damilohun Samuel Metibemu, Sunday Adeola Emaleku, Taoreed Olakunle Owolabi, Nureni Ipinloju","doi":"10.2174/1570163819666220811094019","DOIUrl":null,"url":null,"abstract":"Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates.Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.Results: The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds.Conclusion: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.","PeriodicalId":10858,"journal":{"name":"Current drug discovery technologies","volume":"19 6","pages":"e110822207398"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current drug discovery technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1570163819666220811094019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates.

Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.

Results: The QSAR model of choice satisfied the recommended values and was statistically significant. The R²_pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol^-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds.

Conclusion: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

预测2-烷氧羰基烯丙基酯对MDA-MB-231乳腺癌的抗癌活性——QSAR、机器学习和分子对接。

背景:由于现有药物的失败、耐药性和相关的副作用，乳腺癌和其他形式癌症的死亡率不断增加，这要求开发新的和有效的候选药物。方法:在本研究中，我们使用QSAR和极限学习机模型预测了一些2-烷氧羰基烯丙基酯作为抗MDA-MB-231乳腺癌的潜在候选药物的生物活性。先导候选物被停靠在碳酸酐酶靶标的活性位点上。结果:选择的QSAR模型满足推荐值，具有统计学意义。R2pred(0.6572)是对模型可预测性的信任。极限学习机ELM-Sig模型对MDAMB- 231乳腺癌表现出优异的性能优势。对接分数为4.67 kcal mol-1的化合物22对碳酸酐酶蛋白的抑制效果较好，通过羰基键相互作用。结论:极限学习机的ELM-Sig模型具有优异的性能优势，在寻找新型抗癌药物方面具有一定的应用价值。

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

Current drug discovery technologies Pharmacology, Toxicology and Pharmaceutics-Drug Discovery

CiteScore

3.70

自引率

0.00%

发文量

期刊介绍： Due to the plethora of new approaches being used in modern drug discovery by the pharmaceutical industry, Current Drug Discovery Technologies has been established to provide comprehensive overviews of all the major modern techniques and technologies used in drug design and discovery. The journal is the forum for publishing both original research papers and reviews describing novel approaches and cutting edge technologies used in all stages of drug discovery. The journal addresses the multidimensional challenges of drug discovery science including integration issues of the drug discovery process.