In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Siwen Li, Haojie Xu, Fengxi Liu, Rong Ni, Yinping Shi, Xiao Li
{"title":"In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.","authors":"Siwen Li, Haojie Xu, Fengxi Liu, Rong Ni, Yinping Shi, Xiao Li","doi":"10.1007/s11030-025-11266-8","DOIUrl":null,"url":null,"abstract":"<p><p>Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, a range of machine and deep learning models were developed for five cardiotoxicity end points, including heart failure (HF), arrhythmia (ARR), heart block (HB), hypertension (HP), and heart attack (HA). A total of 110 predictive models were constructed for each cardiotoxicity endpoint using various algorithms and molecular descriptors, and consensus models were developed based on the best-performing individual classifiers. The consensus models consistently outperformed individual models in cross-validation and external validation. Further molecular property analysis revealed that cardiotoxic compounds tend to exhibit higher molecular weight, increased lipophilicity (logP), lower hydrogen bonding capacity (HBD and HBA), and reduced topological polar surface area (TPSA). Additionally, key structural alerts (SAs), including secondary amines, benzene derivatives, sulfonamide/sulfonylurea groups, and heterocyclic structures, were identified. These SAs may mediate cardiotoxicity through ion channel inhibition, oxidative stress induction, and calcium homeostasis disruption. This study provides an integrated machine learning and deep learning computational framework for drug cardiotoxicity assessment and provides an exploration of the structural characteristics of cardiotoxic compounds, which is helpful for the discovery of safer drugs and chemical risk assessment.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Diversity","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s11030-025-11266-8","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, a range of machine and deep learning models were developed for five cardiotoxicity end points, including heart failure (HF), arrhythmia (ARR), heart block (HB), hypertension (HP), and heart attack (HA). A total of 110 predictive models were constructed for each cardiotoxicity endpoint using various algorithms and molecular descriptors, and consensus models were developed based on the best-performing individual classifiers. The consensus models consistently outperformed individual models in cross-validation and external validation. Further molecular property analysis revealed that cardiotoxic compounds tend to exhibit higher molecular weight, increased lipophilicity (logP), lower hydrogen bonding capacity (HBD and HBA), and reduced topological polar surface area (TPSA). Additionally, key structural alerts (SAs), including secondary amines, benzene derivatives, sulfonamide/sulfonylurea groups, and heterocyclic structures, were identified. These SAs may mediate cardiotoxicity through ion channel inhibition, oxidative stress induction, and calcium homeostasis disruption. This study provides an integrated machine learning and deep learning computational framework for drug cardiotoxicity assessment and provides an exploration of the structural characteristics of cardiotoxic compounds, which is helpful for the discovery of safer drugs and chemical risk assessment.

集成机器学习和结构模式识别的药物性心脏毒性的计算机预测。
药物引起的心脏毒性对人类健康构成重大风险,需要可靠的预测模型进行安全性评估。在这项研究中,针对五种心脏毒性终点开发了一系列机器和深度学习模型,包括心力衰竭(HF)、心律失常(ARR)、心脏传导阻滞(HB)、高血压(HP)和心脏病发作(HA)。使用各种算法和分子描述符为每个心脏毒性终点构建了总共110个预测模型,并基于表现最佳的个体分类器建立了共识模型。共识模型在交叉验证和外部验证中始终优于单个模型。进一步的分子性质分析表明,心脏毒性化合物倾向于表现出更高的分子量、更高的亲脂性(logP)、更低的氢键容量(HBD和HBA)和更低的拓扑极性表面积(TPSA)。此外,还确定了关键结构警报(SAs),包括仲胺、苯衍生物、磺胺/磺脲基和杂环结构。这些sa可能通过离子通道抑制、氧化应激诱导和钙稳态破坏介导心脏毒性。本研究为药物心脏毒性评估提供了一个集成的机器学习和深度学习计算框架,并对心脏毒性化合物的结构特征进行了探索,有助于发现更安全的药物和化学风险评估。
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 求助全文
来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信