通过局部-全局自我关注,学习基于图案的药物相互作用预测图谱

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-08-27 DOI:10.1038/s42256-024-00888-6
Yi Zhong, Gaozheng Li, Ji Yang, Houbing Zheng, Yongqiang Yu, Jiheng Zhang, Heng Luo, Biao Wang, Zuquan Weng
{"title":"通过局部-全局自我关注,学习基于图案的药物相互作用预测图谱","authors":"Yi Zhong, Gaozheng Li, Ji Yang, Houbing Zheng, Yongqiang Yu, Jiheng Zhang, Heng Luo, Biao Wang, Zuquan Weng","doi":"10.1038/s42256-024-00888-6","DOIUrl":null,"url":null,"abstract":"Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical research and clinical applications due to the high risk of causing severe adverse drug reactions or drug withdrawals. Many deep learning models have achieved high performance in DDI prediction, but model interpretability to reveal the underlying causes of DDIs has not been extensively explored. Here we propose MeTDDI—a deep learning framework with local–global self-attention and co-attention to learn motif-based graphs for DDI prediction. MeTDDI achieved competitive performance compared with state-of-the-art models. Regarding interpretability, we conducted extensive assessments on 73 drugs with 13,786 DDIs and MeTDDI can precisely explain the structural mechanisms for 5,602 DDIs involving 58 drugs. Besides, MeTDDI shows potential to explain complex DDI mechanisms and mitigate DDI risks. To summarize, MeTDDI provides a new perspective on exploring DDI mechanisms, which will benefit both drug discovery and polypharmacy for safer therapies for patients. A transformer-based approach that predicts drug–drug interactions in polypharmacy has been shown, which also identifies perpetrator drugs and the chemical mechanisms causing the interactions. The method could facilitate high-throughput optimization of drug combinations and mitigate adverse drug–drug interaction risks.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 9","pages":"1094-1105"},"PeriodicalIF":18.8000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention\",\"authors\":\"Yi Zhong, Gaozheng Li, Ji Yang, Houbing Zheng, Yongqiang Yu, Jiheng Zhang, Heng Luo, Biao Wang, Zuquan Weng\",\"doi\":\"10.1038/s42256-024-00888-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical research and clinical applications due to the high risk of causing severe adverse drug reactions or drug withdrawals. Many deep learning models have achieved high performance in DDI prediction, but model interpretability to reveal the underlying causes of DDIs has not been extensively explored. Here we propose MeTDDI—a deep learning framework with local–global self-attention and co-attention to learn motif-based graphs for DDI prediction. MeTDDI achieved competitive performance compared with state-of-the-art models. Regarding interpretability, we conducted extensive assessments on 73 drugs with 13,786 DDIs and MeTDDI can precisely explain the structural mechanisms for 5,602 DDIs involving 58 drugs. Besides, MeTDDI shows potential to explain complex DDI mechanisms and mitigate DDI risks. To summarize, MeTDDI provides a new perspective on exploring DDI mechanisms, which will benefit both drug discovery and polypharmacy for safer therapies for patients. A transformer-based approach that predicts drug–drug interactions in polypharmacy has been shown, which also identifies perpetrator drugs and the chemical mechanisms causing the interactions. The method could facilitate high-throughput optimization of drug combinations and mitigate adverse drug–drug interaction risks.\",\"PeriodicalId\":48533,\"journal\":{\"name\":\"Nature Machine Intelligence\",\"volume\":\"6 9\",\"pages\":\"1094-1105\"},\"PeriodicalIF\":18.8000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Machine Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.nature.com/articles/s42256-024-00888-6\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.nature.com/articles/s42256-024-00888-6","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

意外的药物相互作用(DDIs)是药物研究和临床应用中的重要问题,因为它极有可能导致严重的药物不良反应或停药。许多深度学习模型在 DDI 预测方面取得了很高的性能,但揭示 DDIs 潜在原因的模型可解释性尚未得到广泛探索。在此,我们提出了MeTDDI--一种具有局部-全局自关注和共关注的深度学习框架,用于学习基于主题图的DDI预测。与最先进的模型相比,MeTDDI 的性能极具竞争力。在可解释性方面,我们对73种药物的13786个DDIs进行了广泛评估,MeTDDI可以精确解释58种药物的5602个DDIs的结构机制。此外,MeTDDI 显示出解释复杂 DDI 机制和降低 DDI 风险的潜力。总之,MeTDDI 为探索 DDI 机制提供了一个新的视角,这将有利于药物发现和多药治疗,为患者提供更安全的疗法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

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
Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention
Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical research and clinical applications due to the high risk of causing severe adverse drug reactions or drug withdrawals. Many deep learning models have achieved high performance in DDI prediction, but model interpretability to reveal the underlying causes of DDIs has not been extensively explored. Here we propose MeTDDI—a deep learning framework with local–global self-attention and co-attention to learn motif-based graphs for DDI prediction. MeTDDI achieved competitive performance compared with state-of-the-art models. Regarding interpretability, we conducted extensive assessments on 73 drugs with 13,786 DDIs and MeTDDI can precisely explain the structural mechanisms for 5,602 DDIs involving 58 drugs. Besides, MeTDDI shows potential to explain complex DDI mechanisms and mitigate DDI risks. To summarize, MeTDDI provides a new perspective on exploring DDI mechanisms, which will benefit both drug discovery and polypharmacy for safer therapies for patients. A transformer-based approach that predicts drug–drug interactions in polypharmacy has been shown, which also identifies perpetrator drugs and the chemical mechanisms causing the interactions. The method could facilitate high-throughput optimization of drug combinations and mitigate adverse drug–drug interaction risks.
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
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
期刊最新文献
Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning A soft skin with self-decoupled three-axis force-sensing taxels Efficient rare event sampling with unsupervised normalizing flows Clinical large language models with misplaced focus Efficient generation of protein pockets with PocketGen
×
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