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In this study, we proposed a machine learning hybrid model Graph Transformer Integrated Encoder (GTIE-RT) for mapping drugs to target metabolic pathways in human. The proposed model is a composite of a Graph Convolution Network (GCN) and transformer encoder for graph embedding and attention mechanism. The output of the transformer encoder is then fed into the Extremely Randomized Trees Classifier to predict target metabolic pathways. The evaluation of the GTIE-RT on drugs dataset demonstrates excellent performance metrics, including accuracy (&gt;95%), recall (&gt;92%), precision (&gt;93%) and F1-score (&gt;92%). Compared to other variants and machine learning methods, GTIE-RT consistently shows more reliable results. <\/jats:p>","DOI":"10.1142\/s0219720024500100","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T08:31:20Z","timestamp":1714120280000},"source":"Crossref","is-referenced-by-count":3,"title":["Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7043-8081","authenticated-orcid":false,"given":"Hayat Ali","family":"Shah","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7415","authenticated-orcid":false,"given":"Juan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Artificial 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