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However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https:\/\/github.com\/KEAML-JLU\/MSI-DTI.<\/jats:p>","DOI":"10.1093\/bib\/bbae238","type":"journal-article","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T11:24:31Z","timestamp":1716117871000},"source":"Crossref","is-referenced-by-count":27,"title":["MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention"],"prefix":"10.1093","volume":"25","author":[{"given":"Wenchuan","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin , China"}]},{"given":"Yufeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin , China"}]},{"given":"Guosheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin , China"}]},{"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[{"name":"Zhuhai Laboratory of the Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , Zhuhai College of Science and Technology, Zhuhai 519041 , China"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 , USA"}]},{"given":"Xiaoyue","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7162-7826","authenticated-orcid":false,"given":"Renchu","family":"Guan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin , China"}]}],"member":"286","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"issue":"5","key":"2024051911242305200_ref1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","article-title":"Rethinking drug design in the artificial intelligence era","volume":"19","author":"Petra Schneider","year":"2020","journal-title":"Nat Rev Drug Discov"},{"key":"2024051911242305200_ref2","doi-asserted-by":"crossref","first-page":"103159","DOI":"10.1016\/j.jbi.2019.103159","article-title":"A comprehensive review of feature based methods for drug target interaction prediction","volume":"93","author":"Sachdev","year":"2019","journal-title":"J Biomed Inform"},{"key":"2024051911242305200_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2193-9616-1-17","article-title":"Drug-target and disease networks: polypharmacology in the post-genomic era","volume":"1","author":"Masoudi-Nejad","year":"2013","journal-title":"In Silico Pharmacology"},{"issue":"5","key":"2024051911242305200_ref4","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1038\/nprot.2016.051","article-title":"Computational protein\u2013ligand docking and virtual drug screening with the autodock suite","volume":"11","author":"Forli","year":"2016","journal-title":"Nat Protoc"},{"issue":"2","key":"2024051911242305200_ref5","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nbt1284","article-title":"Relating protein pharmacology by ligand chemistry","volume":"25","author":"Keiser","year":"2007","journal-title":"Nat Biotechnol"},{"issue":"7873","key":"2024051911242305200_ref6","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with alphafold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2024051911242305200_ref7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: principles, practice, and opportunities","volume":"50","author":"Zitnik","year":"2019","journal-title":"Inf Fusion"},{"issue":"4","key":"2024051911242305200_ref8","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1093\/bib\/bby010","article-title":"Open-source chemogenomic data-driven algorithms for predicting drug\u2013target interactions","volume":"20","author":"Hao","year":"2019","journal-title":"Brief Bioinform"},{"issue":"1","key":"2024051911242305200_ref9","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"key":"2024051911242305200_ref10","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2024051911242305200_ref11","first-page":"1871","article-title":"Liblinear: a library for large linear classification. 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