{"title":"DeFuseDTI:采用双分支编码器和多视图融合的可解释药物靶点相互作用预测模型","authors":"","doi":"10.1016/j.future.2024.07.014","DOIUrl":null,"url":null,"abstract":"<div><p>Predicting the interaction between drugs and targets is a crucial step in drug development, and computer-based deep learning approaches have the potential to significantly reduce costs. Existing models using a single encoder often suffer from insufficient cross-modal feature extraction, with most models tending to overly focus on extracting locally aggregated information, thereby diluting the detailed features of each target residue and drug atom. Additionally, the lack of effective interaction fusion between drug and target lead to prediction results lacking reliable interpretability, posing a more urgent issue. To address these challenges, we propose a dual-branch encoder model, DeFuseDTI, which includes base encoder and detail encoder to extract locally aggregated features and detailed features of each target residue and drug atom. The detail encoder (utilizing Invertible Neural Networks for targets and graph transformers for drugs) can capture furtherly the features of each atom and residue, providing rich and precise features for model interpretability. For better achieve interactive learning of drug and target features, the Multiview Fusion Attention learning module was introduced to integrate multiview features and generate a unified representations for decoding prediction results. Based on the module's unique attention mechanism, drug-target importance matrices can be obtained, which offer interpretable analysis at the molecular level. Experimental results and analyses demonstrate that DeFuseDTI outperforms several state-of-the-art models on four public datasets. Its significant interpretability holds promise for providing accurate and scientifically meaningful guidance for biochemical experiments at the molecular level.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DeFuseDTI: Interpretable drug target interaction prediction model with dual-branch encoder and multiview fusion\",\"authors\":\"\",\"doi\":\"10.1016/j.future.2024.07.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Predicting the interaction between drugs and targets is a crucial step in drug development, and computer-based deep learning approaches have the potential to significantly reduce costs. Existing models using a single encoder often suffer from insufficient cross-modal feature extraction, with most models tending to overly focus on extracting locally aggregated information, thereby diluting the detailed features of each target residue and drug atom. Additionally, the lack of effective interaction fusion between drug and target lead to prediction results lacking reliable interpretability, posing a more urgent issue. To address these challenges, we propose a dual-branch encoder model, DeFuseDTI, which includes base encoder and detail encoder to extract locally aggregated features and detailed features of each target residue and drug atom. The detail encoder (utilizing Invertible Neural Networks for targets and graph transformers for drugs) can capture furtherly the features of each atom and residue, providing rich and precise features for model interpretability. For better achieve interactive learning of drug and target features, the Multiview Fusion Attention learning module was introduced to integrate multiview features and generate a unified representations for decoding prediction results. Based on the module's unique attention mechanism, drug-target importance matrices can be obtained, which offer interpretable analysis at the molecular level. Experimental results and analyses demonstrate that DeFuseDTI outperforms several state-of-the-art models on four public datasets. Its significant interpretability holds promise for providing accurate and scientifically meaningful guidance for biochemical experiments at the molecular level.</p></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X24003741\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24003741","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
DeFuseDTI: Interpretable drug target interaction prediction model with dual-branch encoder and multiview fusion
Predicting the interaction between drugs and targets is a crucial step in drug development, and computer-based deep learning approaches have the potential to significantly reduce costs. Existing models using a single encoder often suffer from insufficient cross-modal feature extraction, with most models tending to overly focus on extracting locally aggregated information, thereby diluting the detailed features of each target residue and drug atom. Additionally, the lack of effective interaction fusion between drug and target lead to prediction results lacking reliable interpretability, posing a more urgent issue. To address these challenges, we propose a dual-branch encoder model, DeFuseDTI, which includes base encoder and detail encoder to extract locally aggregated features and detailed features of each target residue and drug atom. The detail encoder (utilizing Invertible Neural Networks for targets and graph transformers for drugs) can capture furtherly the features of each atom and residue, providing rich and precise features for model interpretability. For better achieve interactive learning of drug and target features, the Multiview Fusion Attention learning module was introduced to integrate multiview features and generate a unified representations for decoding prediction results. Based on the module's unique attention mechanism, drug-target importance matrices can be obtained, which offer interpretable analysis at the molecular level. Experimental results and analyses demonstrate that DeFuseDTI outperforms several state-of-the-art models on four public datasets. Its significant interpretability holds promise for providing accurate and scientifically meaningful guidance for biochemical experiments at the molecular level.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.