基于机器学习和物理方法发现新型 NLRP3 抑制剂

IF 4.3 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY BMC Chemistry Pub Date : 2024-10-28 DOI:10.1186/s13065-024-01323-y
Tao Jiang, Shijing Qian, Jinhong Xu, Shuihong Yu, Yang Lu, Linsheng Xu, Xiaosi Yang
{"title":"基于机器学习和物理方法发现新型 NLRP3 抑制剂","authors":"Tao Jiang,&nbsp;Shijing Qian,&nbsp;Jinhong Xu,&nbsp;Shuihong Yu,&nbsp;Yang Lu,&nbsp;Linsheng Xu,&nbsp;Xiaosi Yang","doi":"10.1186/s13065-024-01323-y","DOIUrl":null,"url":null,"abstract":"<div><p>The NLRP3 inflammasome plays a crucial role in inflammatory responses, particularly in alcohol-related liver disease (ALD). Given that NLRP3 has emerged as a potential therapeutic target for ALD, the development of effective inhibitors is of great importance. In this study, we trained 11 regression models, and the results showed that LightGBM, Random Forest, and XGBoost performed the best, achieving R² values of 0.774, 0.755, and 0.719, respectively. Using machine learning models and physical methods, we screened more than 11.5 million compounds from Asinex, Princeton, UkrOrgSynthesis, Chemdiv, Chembridge, Alinda, Enamine, and Lifechemicals, which led to the identification of 26 potential NLRP3 inhibitors. Furthermore, molecular dynamics simulations and MMGBSA binding energy calculations confirmed the stability of the interactions between NLRP3 and three key molecules: 19,655,631 (source Chembridge), 38,214,692 (source Chembridge), and Z1180203703 (source Enamine). Additionally, ADMET analysis revealed their favorable pharmacokinetic properties. This study provides insights and candidate molecules for discovering NLRP3 inhibitors, potentially applicable in treating related diseases.</p></div>","PeriodicalId":496,"journal":{"name":"BMC Chemistry","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bmcchem.biomedcentral.com/counter/pdf/10.1186/s13065-024-01323-y","citationCount":"0","resultStr":"{\"title\":\"Discovery of novel NLRP3 inhibitors based on machine learning and physical methods\",\"authors\":\"Tao Jiang,&nbsp;Shijing Qian,&nbsp;Jinhong Xu,&nbsp;Shuihong Yu,&nbsp;Yang Lu,&nbsp;Linsheng Xu,&nbsp;Xiaosi Yang\",\"doi\":\"10.1186/s13065-024-01323-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The NLRP3 inflammasome plays a crucial role in inflammatory responses, particularly in alcohol-related liver disease (ALD). Given that NLRP3 has emerged as a potential therapeutic target for ALD, the development of effective inhibitors is of great importance. In this study, we trained 11 regression models, and the results showed that LightGBM, Random Forest, and XGBoost performed the best, achieving R² values of 0.774, 0.755, and 0.719, respectively. Using machine learning models and physical methods, we screened more than 11.5 million compounds from Asinex, Princeton, UkrOrgSynthesis, Chemdiv, Chembridge, Alinda, Enamine, and Lifechemicals, which led to the identification of 26 potential NLRP3 inhibitors. Furthermore, molecular dynamics simulations and MMGBSA binding energy calculations confirmed the stability of the interactions between NLRP3 and three key molecules: 19,655,631 (source Chembridge), 38,214,692 (source Chembridge), and Z1180203703 (source Enamine). Additionally, ADMET analysis revealed their favorable pharmacokinetic properties. This study provides insights and candidate molecules for discovering NLRP3 inhibitors, potentially applicable in treating related diseases.</p></div>\",\"PeriodicalId\":496,\"journal\":{\"name\":\"BMC Chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://bmcchem.biomedcentral.com/counter/pdf/10.1186/s13065-024-01323-y\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s13065-024-01323-y\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Chemistry","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1186/s13065-024-01323-y","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

NLRP3 炎性体在炎症反应中发挥着至关重要的作用,尤其是在酒精相关肝病(ALD)中。鉴于 NLRP3 已成为 ALD 的潜在治疗靶点,开发有效的抑制剂至关重要。在这项研究中,我们训练了 11 个回归模型,结果表明 LightGBM、随机森林和 XGBoost 表现最佳,R² 值分别为 0.774、0.755 和 0.719。利用机器学习模型和物理方法,我们筛选了来自 Asinex、Princeton、UkrOrgSynthesis、Chemdiv、Chembridge、Alinda、Enamine 和 Lifechemicals 的 1150 多万种化合物,从而鉴定出 26 种潜在的 NLRP3 抑制剂。此外,分子动力学模拟和 MMGBSA 结合能计算证实了 NLRP3 与三种关键分子之间相互作用的稳定性:19,655,631(来源 Chembridge)、38,214,692(来源 Chembridge)和 Z1180203703(来源 Enamine)。此外,ADMET 分析还显示了它们良好的药代动力学特性。这项研究为发现 NLRP3 抑制剂提供了见解和候选分子,可能适用于治疗相关疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Discovery of novel NLRP3 inhibitors based on machine learning and physical methods

The NLRP3 inflammasome plays a crucial role in inflammatory responses, particularly in alcohol-related liver disease (ALD). Given that NLRP3 has emerged as a potential therapeutic target for ALD, the development of effective inhibitors is of great importance. In this study, we trained 11 regression models, and the results showed that LightGBM, Random Forest, and XGBoost performed the best, achieving R² values of 0.774, 0.755, and 0.719, respectively. Using machine learning models and physical methods, we screened more than 11.5 million compounds from Asinex, Princeton, UkrOrgSynthesis, Chemdiv, Chembridge, Alinda, Enamine, and Lifechemicals, which led to the identification of 26 potential NLRP3 inhibitors. Furthermore, molecular dynamics simulations and MMGBSA binding energy calculations confirmed the stability of the interactions between NLRP3 and three key molecules: 19,655,631 (source Chembridge), 38,214,692 (source Chembridge), and Z1180203703 (source Enamine). Additionally, ADMET analysis revealed their favorable pharmacokinetic properties. This study provides insights and candidate molecules for discovering NLRP3 inhibitors, potentially applicable in treating related diseases.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BMC Chemistry
BMC Chemistry Chemistry-General Chemistry
CiteScore
5.30
自引率
2.20%
发文量
92
审稿时长
27 weeks
期刊介绍: BMC Chemistry, formerly known as Chemistry Central Journal, is now part of the BMC series journals family. Chemistry Central Journal has served the chemistry community as a trusted open access resource for more than 10 years – and we are delighted to announce the next step on its journey. In January 2019 the journal has been renamed BMC Chemistry and now strengthens the BMC series footprint in the physical sciences by publishing quality articles and by pushing the boundaries of open chemistry.
期刊最新文献
Separation properties and fouling resistance of polyethersulfone membrane modified by fungal chitosan Removal of As(V) and Cr(VI) using quinoxaline chitosan schiff base: synthesis, characterization and adsorption mechanism Novel pyrrole based triazole moiety as therapeutic hybrid: synthesis, characterization and anti-Alzheimer potential with molecular mechanism of protein ligand profile Isolation of highly polar galloyl glucoside tautomers from Saxifraga tangutica through preparative chromatography and assessment of their in vitro antioxidant activity La-supported SnO2–CaO composite catalysts for efficient malachite green degradation under UV–vis light
×
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