{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:31:53Z","timestamp":1771065113891,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62222311"],"award-info":[{"award-number":["62222311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072329"],"award-info":[{"award-number":["62072329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Plan Project","doi-asserted-by":"publisher","award":["22JCZDJC00580"],"award-info":[{"award-number":["22JCZDJC00580"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Support Plan for High level Talents in Zhejiang Province","award":["2021R52019"],"award-info":[{"award-number":["2021R52019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Effective computational methods for predicting the mechanism of action (MoA) of compounds are essential in drug discovery. Current MoA prediction models mainly utilize the structural information of compounds. However, high-throughput screening technologies have generated more targeted cell perturbation data for MoA prediction, a factor frequently disregarded by the majority of current approaches. Moreover, exploring the commonalities and specificities among different fingerprint representations remains challenging.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this paper, we propose IFMoAP, a model integrating cell perturbation image and fingerprint data for MoA prediction. Firstly, we modify the Res-Net to accommodate the feature extraction of five-channel cell perturbation images and establish a granularity-level attention mechanism to combine coarse- and fine-grained features. To learn both common and specific fingerprint features, we introduce an FP-CS module, projecting four fingerprint embeddings into distinct spaces and incorporating two loss functions for effective learning. Finally, we construct two independent classifiers based on image and fingerprint features for prediction and for weighting the two prediction scores. Experimental results demonstrate that our model achieves highest accuracy of 0.941 when using multimodal data. The comparison with other methods and explorations further highlights the superiority of our proposed model and the complementary characteristics of multimodal data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is available at https:\/\/github.com\/ s1mplehu\/IFMoAP. The raw image data of Cell Painting can be accessed from Figshare (https:\/\/doi.org\/10.17044\/scilifelab.21378906).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf223","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T15:15:39Z","timestamp":1748963739000},"source":"Crossref","is-referenced-by-count":1,"title":["Synergizing multimodal data and fingerprint space exploration for mechanism of action prediction"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1537-6614","authenticated-orcid":false,"given":"Kaimiao","family":"Hu","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University , Tianjin, 300072,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University , Tianjin, 300072,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changming","family":"Sun","sequence":"additional","affiliation":[{"name":"CSIRO Data61 , Sydney, 2000,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Chest Hospital, Tianjin University , Tianjin, 300041,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information, Xiamen University , Xiamen, 361005,","place":["China"]},{"name":"Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University , Macao SAR, 999078,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Life Science and Medicine, Zhejiang Sci-Tech University , Hangzhou, 310023,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-0364","authenticated-orcid":false,"given":"Ran","family":"Su","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University , Tianjin, 300072,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"2025070408274324100_btaf223-B1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s12033-010-9251-z","article-title":"Cell-based assays for high-throughput screening","volume":"45","author":"An","year":"2010","journal-title":"Mol Biotechnol"},{"key":"2025070408274324100_btaf223-B2","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1080\/17460441.2017.1353494","article-title":"Representation and identification of activity cliffs","volume":"12","author":"Bajorath","year":"2017","journal-title":"Expert Opin Drug Discov"},{"key":"2025070408274324100_btaf223-B3","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.comtox.2018.06.004","article-title":"High-accuracy prediction of mechanisms of action using structural alerts","volume":"7","author":"Bauer","year":"2018","journal-title":"Comput Toxicol"},{"key":"2025070408274324100_btaf223-B4","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1038\/nprot.2016.105","article-title":"Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes","volume":"11","author":"Bray","year":"2016","journal-title":"Nat Protoc"},{"key":"2025070408274324100_btaf223-B5","doi-asserted-by":"crossref","first-page":"115526","DOI":"10.18632\/oncotarget.23308","article-title":"Curcumol potentiates celecoxib-induced growth inhibition and apoptosis in human non-small cell lung cancer","volume":"8","author":"Cai","year":"2017","journal-title":"Oncotarget"},{"key":"2025070408274324100_btaf223-B6","doi-asserted-by":"crossref","first-page":"bbac408","DOI":"10.1093\/bib\/bbac408","article-title":"Fp-gnn: a versatile deep learning architecture for enhanced molecular property prediction","volume":"23","author":"Cai","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025070408274324100_btaf223-B7","doi-asserted-by":"crossref","first-page":"ar49","DOI":"10.1091\/mbc.E21-11-0538","article-title":"Cell painting predicts impact of lung cancer variants","volume":"33","author":"Caicedo","year":"2022","journal-title":"Mol Biol Cell"},{"key":"2025070408274324100_btaf223-B8","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1038\/s41467-024-46089-y","article-title":"Drug target prediction through deep learning functional representation of gene signatures","volume":"15","author":"Chen","year":"2024","journal-title":"Nat Commun"},{"key":"2025070408274324100_btaf223-B9","doi-asserted-by":"crossref","first-page":"263","DOI":"10.2174\/157340907782799372","article-title":"Nonlinear SVM approaches to QSPR\/QSAR studies and drug design","volume":"3","author":"Doucet","year":"2007","journal-title":"Curr Comput Aided Drug Des"},{"key":"2025070408274324100_btaf223-B10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/01635581.2020.1749676","article-title":"Therapeutic effects of curcumol in several diseases; an overview","volume":"73","author":"Hashem","year":"2021","journal-title":"Nutr Cancer"},{"key":"2025070408274324100_btaf223-B11","first-page":"770","author":"He","year":"2016"},{"key":"2025070408274324100_btaf223-B12","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1021\/acs.jcim.8b00670","article-title":"Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks","volume":"59","author":"Hofmarcher","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2025070408274324100_btaf223-B13","first-page":"4700","author":"Huang","year":"2017"},{"key":"2025070408274324100_btaf223-B14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-023-00876-4","article-title":"A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions","volume":"11","author":"Khemani","year":"2024","journal-title":"J Big Data"},{"key":"2025070408274324100_btaf223-B15","author":"Kipf","year":"2016"},{"key":"2025070408274324100_btaf223-B16","first-page":"12","article-title":"Exploring different approaches to improve the success of drug discovery and development projects: a review","volume":"6","author":"Kiriiri","year":"2020","journal-title":"Fut J Pharm Sci"},{"key":"2025070408274324100_btaf223-B17","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":"2025070408274324100_btaf223-B18","doi-asserted-by":"crossref","first-page":"11218","DOI":"10.1109\/TNNLS.2023.3250324","article-title":"Meta learning with graph attention networks for low-data drug discovery","volume":"35","author":"Lv","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2025070408274324100_btaf223-B20","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jcc.21276","article-title":"Protein\u2013protein docking dealing with the unknown","volume":"31","author":"Moreira","year":"2010","journal-title":"J Comput Chem"},{"key":"2025070408274324100_btaf223-B21","doi-asserted-by":"crossref","first-page":"648","DOI":"10.2174\/1389200217666160322143631","article-title":"The role of therapeutic drugs on acquired mitochondrial toxicity","volume":"17","author":"Mor\u00e9n","year":"2016","journal-title":"Curr Drug Metab"},{"key":"2025070408274324100_btaf223-B22","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/978-1-59745-177-2_19","article-title":"Molecular docking","volume":"443","author":"Morris","year":"2008","journal-title":"Methods Mol Biol"},{"key":"2025070408274324100_btaf223-B23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s42826-022-00128-1","article-title":"Role of animal models in biomedical research: a review","volume":"38","author":"Mukherjee","year":"2022","journal-title":"Lab Anim Res"},{"key":"2025070408274324100_btaf223-B24","doi-asserted-by":"crossref","first-page":"461","DOI":"10.12659\/MSM.908430","article-title":"Curcumol controls choriocarcinoma stem-like cells self-renewal via repression of DNA methyltransferase (DNMT)-and histone deacetylase (HDAC)-mediated epigenetic regulation","volume":"24","author":"Peng","year":"2018","journal-title":"Med Sci Monit"},{"key":"2025070408274324100_btaf223-B25","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1021\/ci900203n","article-title":"Application of random forest approach to QSAR prediction of aquatic toxicity","volume":"49","author":"Polishchuk","year":"2009","journal-title":"J Chem Inf Model"},{"key":"2025070408274324100_btaf223-B26","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1124\/pr.112.007336","article-title":"Computational methods in drug discovery","volume":"66","author":"Sliwoski","year":"2014","journal-title":"Pharmacol Rev"},{"key":"2025070408274324100_btaf223-B27","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J Chem Inf Comput Sci"},{"key":"2025070408274324100_btaf223-B28","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1038\/nrd1500","article-title":"Biochemical mechanisms of drug action: what does it take for success?","volume":"3","author":"Swinney","year":"2004","journal-title":"Nat Rev Drug Discov"},{"key":"2025070408274324100_btaf223-B29","first-page":"6105","author":"Tan","year":"2019"},{"key":"2025070408274324100_btaf223-B30","first-page":"100060","article-title":"Combining molecular and cell painting image data for mechanism of action prediction","volume":"3","author":"Tian","year":"2023","journal-title":"Artific Intell Life Sci"},{"key":"2025070408274324100_btaf223-B31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","article-title":"Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR","volume":"23","author":"Tropsha","year":"2024","journal-title":"Nat Rev Drug Discov"},{"key":"2025070408274324100_btaf223-B32","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.3390\/molecules25204723","article-title":"Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches","volume":"25","author":"V\u00e1zquez","year":"2020","journal-title":"Molecules"},{"key":"2025070408274324100_btaf223-B33","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"2025070408274324100_btaf223-B34","doi-asserted-by":"crossref","first-page":"7588","DOI":"10.1109\/JBHI.2024.3402529","article-title":"GENNDTI: drug\u2013target interaction prediction using graph neural network enhanced by router nodes","volume":"28","author":"Yang","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025070408274324100_btaf223-B35","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1038\/s43588-024-00679-4","article-title":"Deep learning large-scale drug discovery and repurposing","volume":"4","author":"Yu","year":"2024","journal-title":"Nat Comput Sci"},{"key":"2025070408274324100_btaf223-B36","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.fct.2017.10.021","article-title":"Development of novel prediction model for drug-induced mitochondrial toxicity by using na\u00efve Bayes classifier method","volume":"110","author":"Zhang","year":"2017","journal-title":"Food Chem Toxicol"},{"key":"2025070408274324100_btaf223-B37","first-page":"6848","author":"Zhang","year":"2018"},{"key":"2025070408274324100_btaf223-B38","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.tox.2017.07.009","article-title":"Clinical effects of chemical exposures on mitochondrial function","volume":"391","author":"Zolkipli-Cunningham","year":"2017","journal-title":"Toxicology"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf223\/63427214\/btaf223.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/6\/btaf223\/63427214\/btaf223.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/6\/btaf223\/63427214\/btaf223.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T12:27:55Z","timestamp":1751632075000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf223\/8155844"}},"subtitle":[],"editor":[{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf223","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,6]]},"published":{"date-parts":[[2025,6]]},"article-number":"btaf223"}}