{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:39:15Z","timestamp":1775263155509,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction. After training on the L1000 dataset, MiTCP achieves an average Pearson correlation coefficient (PCC) of 0.482 on the test set and an average PCC of 0.801 for predicting the top 50 differentially expressed genes, which outperforms other existing methods. Furthermore, we used MiTCP to predict CTPs of three cancer drugs, palbociclib, irinotecan and goserelin, and performed gene enrichment analysis on the top differentially expressed genes and found that the enriched pathways and Gene Ontology terms are highly relevant to the corresponding diseases, which reveals the potential of MiTCP in drug development.<\/jats:p>","DOI":"10.1093\/bib\/bbaf006","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T15:19:01Z","timestamp":1737645541000},"source":"Crossref","is-referenced-by-count":3,"title":["Predicting transcriptional changes induced by molecules with MiTCP"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9041-159X","authenticated-orcid":false,"given":"Kaiyuan","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7601-4800","authenticated-orcid":false,"given":"Jiabei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghao","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5010-464X","authenticated-orcid":false,"given":"Xiaoyong","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Automation , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240,","place":["China"]},{"name":"State Key Laboratory of Biopharmaceutical Preparation and Delivery , Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, Beijing 100190,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"2025012315184391100_ref1","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1038\/nrd.2017.226","article-title":"Drug development in the era of precision medicine","volume":"17","author":"Dugger","year":"2018","journal-title":"Nat Rev Drug Discov"},{"key":"2025012315184391100_ref2","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1038\/nrd2593","article-title":"Pharmacogenetics in drug discovery and development: A translational perspective","volume":"7","author":"Roses","year":"2008","journal-title":"Nat Rev Drug Discov"},{"key":"2025012315184391100_ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/proteomes11010001","article-title":"The need for biomarkers in the ALS\u2013FTD spectrum: A clinical point of view on the role of proteomics","volume":"11","author":"Vignaroli","year":"2023","journal-title":"Proteomes"},{"key":"2025012315184391100_ref4","doi-asserted-by":"publisher","first-page":"bbab378","DOI":"10.1093\/bib\/bbab378","article-title":"How much can deep learning improve prediction of the responses to drugs in cancer cell lines?","volume":"23","author":"Chen","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025012315184391100_ref5","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1124\/pr.55.4.4","article-title":"International Union of Pharmacology Committee on receptor nomenclature and drug classification. 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