iSKIN: Integrated application of machine learning and Mondrian conformal prediction to detect skin sensitizers in cosmetic raw materials

SmartMat Pub Date : 2024-02-15 DOI:10.1002/smm2.1278
Weikaixin Kong, Jie Zhu, Peipei Shan, Huiyan Ying, Tongyu Chen, Bowen Zhang, Chao Peng, Zihan Wang, Yifan Wang, Liting Huang, Suzhen Bi, Weining Ma, Zhuo Huang, Sujie Zhu, Xueyan Liu, Chun Li
{"title":"iSKIN: Integrated application of machine learning and Mondrian conformal prediction to detect skin sensitizers in cosmetic raw materials","authors":"Weikaixin Kong, Jie Zhu, Peipei Shan, Huiyan Ying, Tongyu Chen, Bowen Zhang, Chao Peng, Zihan Wang, Yifan Wang, Liting Huang, Suzhen Bi, Weining Ma, Zhuo Huang, Sujie Zhu, Xueyan Liu, Chun Li","doi":"10.1002/smm2.1278","DOIUrl":null,"url":null,"abstract":"Animal experiments traditionally identify sensitizers in cosmetic materials. However, with growing concerns over animal ethics and bans on such experiments globally, alternative methods like machine learning are gaining prominence for their efficiency and cost‐effectiveness. In this study, to develop a robust sensitizer detector model, we first constructed benchmark data sets using data from previous studies and a public database, then 589 sensitizers and 831 nonsensitizers were collected. In addition, a graph‐based autoencoder and Mondrian conformal prediction (MCP) were combined to build a robust sensitizer detector, iSKIN. In the independent test set, the Matthews correlation coefficient (MCC) and the area under the receiver operating characteristic curve (ROCAUC) values of the iSKIN model without MCP were 0.472 and 0.804, respectively, which are higher than those of the three baseline models. When setting the significance level in MCP at 0.7, the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927, respectively. Regrouping experiments proved that the MCP method is robust in the improvement of model performance. Through key structure analysis, seven key substructures in sensitizers were identified to guide cosmetic material design. Notably, long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho‐positions were potential sensitizers. Finally, a user‐friendly web tool (http://www.iskin.work/) of the iSKIN model was deployed to be used by other researchers. In summary, the proposed iSKIN model has achieved state‐of‐the‐art performance so far, which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials.","PeriodicalId":21794,"journal":{"name":"SmartMat","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SmartMat","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/smm2.1278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Animal experiments traditionally identify sensitizers in cosmetic materials. However, with growing concerns over animal ethics and bans on such experiments globally, alternative methods like machine learning are gaining prominence for their efficiency and cost‐effectiveness. In this study, to develop a robust sensitizer detector model, we first constructed benchmark data sets using data from previous studies and a public database, then 589 sensitizers and 831 nonsensitizers were collected. In addition, a graph‐based autoencoder and Mondrian conformal prediction (MCP) were combined to build a robust sensitizer detector, iSKIN. In the independent test set, the Matthews correlation coefficient (MCC) and the area under the receiver operating characteristic curve (ROCAUC) values of the iSKIN model without MCP were 0.472 and 0.804, respectively, which are higher than those of the three baseline models. When setting the significance level in MCP at 0.7, the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927, respectively. Regrouping experiments proved that the MCP method is robust in the improvement of model performance. Through key structure analysis, seven key substructures in sensitizers were identified to guide cosmetic material design. Notably, long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho‐positions were potential sensitizers. Finally, a user‐friendly web tool (http://www.iskin.work/) of the iSKIN model was deployed to be used by other researchers. In summary, the proposed iSKIN model has achieved state‐of‐the‐art performance so far, which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials.
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
iSKIN:综合应用机器学习和蒙德里安保形预测技术检测化妆品原料中的皮肤致敏物质
传统上,通过动物实验来识别化妆品材料中的致敏物质。然而,随着人们对动物伦理的日益关注以及全球对此类实验的禁止,机器学习等替代方法因其高效性和成本效益而日益受到重视。在本研究中,为了开发出一种稳健的敏化物检测模型,我们首先利用以往研究的数据和公共数据库构建了基准数据集,然后收集了 589 种敏化物和 831 种非敏化物。此外,我们还将基于图的自动编码器和蒙德里安保形预测(MCP)结合起来,建立了鲁棒的敏化剂检测器 iSKIN。在独立测试集中,无 MCP 的 iSKIN 模型的马修斯相关系数(MCC)和接收者工作特征曲线下面积(ROCAUC)值分别为 0.472 和 0.804,均高于三个基线模型。当 MCP 的显著性水平设定为 0.7 时,iSKIN 模型的 MCC 和 ROCAUC 值分别达到 0.753 和 0.927。重新分组实验证明,MCP 方法在改善模型性能方面是稳健的。通过关键结构分析,确定了敏化剂中的七个关键子结构,为化妆品材料设计提供了指导。值得注意的是,含有卤素原子的长链和在正交位置含有两个氯原子的苯基是潜在的敏化剂。最后,iSKIN 模型的用户友好型网络工具(http://www.iskin.work/)已部署完毕,可供其他研究人员使用。总之,所提出的 iSKIN 模型达到了目前最先进的性能,可为化妆品原料的安全性评价做出贡献,并为这些原料的化学结构设计提供参考。
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
自引率
0.00%
发文量
0
期刊最新文献
Chiral gypsum with high‐performance mechanical properties induced by self‐assembly of chiral amino acid on an amorphous mineral Electrolyte‐gated organic field‐effect transistors with high operational stability and lifetime in practical electrolytes Efforts of implementing ultra‐flexible thin‐film encapsulation for optoelectronic devices based on atomic layer deposition technology Flexible retinomorphic vision sensors with scotopic and photopic adaptation for a fully flexible neuromorphic machine vision system Coral‐inspired anti‐biofilm therapeutic abutments as a new paradigm for prevention and treatment of peri‐implant infection
×
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