<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Li Y</submitter><funding>Shanxi Province Science Foundation for Youths</funding><funding>Natural Science Foundation of Shanxi</funding><funding>Foundation for Innovative Research Groups of the National Natural Science Foundation of China</funding><funding>National Natural Science Foundation of China</funding><funding>Research Project Supported by Shanxi Scholarship Council of China</funding><funding>National Key Scientific and Technological Infrastructure project "Earth System Numerical Simulation Facility"</funding><funding>National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility”</funding><pagination>25054</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11499656</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>14(1)</volume><pubmed_abstract>Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and prepro</pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.</pubmed_title><pmcid>PMC11499656</pmcid><funding_grant_id>20210302123092</funding_grant_id><funding_grant_id>62403344</funding_grant_id><funding_grant_id>62176177</funding_grant_id><funding_grant_id>2021-039</funding_grant_id><funding_grant_id>20210302123112</funding_grant_id><funding_grant_id>2023-EL-PT-000371, 2023-EL-PT-000374</funding_grant_id><pubmed_authors>Yang H</pubmed_authors><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Chen Z</pubmed_authors><pubmed_authors>Wang B</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Xiang J</pubmed_authors><pubmed_authors>Yan T</pubmed_authors><pubmed_authors>Wang H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.</name><description>Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and prepro</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Oct</publication><modification>2025-04-26T00:14:42.208Z</modification><creation>2025-04-06T09:39:33.379Z</creation></dates><accession>S-EPMC11499656</accession><cross_references><pubmed>39443630</pubmed><doi>10.1038/s41598-024-76367-0</doi></cross_references></HashMap>