{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:36:58Z","timestamp":1775579818735,"version":"3.50.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The mechanism of action is an important aspect of drug development. It can help scientists in the process of drug discovery. This paper provides a machine learning model to predict the mechanism of action of a drug. The machine learning models used in this paper are Binary Relevance K Nearest Neighbors (Type A and Type B), Multi-label K-Nearest Neighbors and a custom neural network. These machine learning models are evaluated using the mean column-wise log loss. The custom neural network model had the best accuracy with a log loss of 0.01706. This neural network model is integrated into a web application using Flask framework. A user can upload a custom testing features dataset, which contains the gene expression and the cell viability levels. The web application will output the top classes of drugs, along with the scatter plots for each of the drug.<\/jats:p>","DOI":"10.1007\/s44163-021-00012-2","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T13:02:42Z","timestamp":1637758962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Classification of drugs based on mechanism of action using machine learning techniques"],"prefix":"10.1007","volume":"1","author":[{"given":"H. L.","family":"Gururaj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Flammini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H. A. Chaya","family":"Kumari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. R.","family":"Puneeth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. R. Sunil","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"12_CR1","unstructured":"Definition of mechanism of action, National Cancer Institute, https:\/\/www.cancer.gov\/publications\/dictionaries\/cancer-terms\/def\/mechanism-of-action."},{"key":"12_CR2","unstructured":"Pierre-Louis T. Mechanism of drug action and pharmacokinetics\/pharmacodynamics integration in dosage regimen optimization for veterinary medicine. Veterinary Pharmacology and Therapeutics. Wiley. p 1525, 2018, 9781118855829. hal-02787306."},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nchembio.2010","volume":"12","author":"A Palmer","year":"2016","unstructured":"Palmer A. The many genes of drug mechanism. Nat Chem Biol. 2016;12:57\u20138. https:\/\/doi.org\/10.1038\/nchembio.2010.","journal-title":"Nat Chem Biol"},{"issue":"2","key":"12_CR4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1039\/d0cb00226g","volume":"2","author":"T Yuan","year":"2021","unstructured":"Yuan T, et al. The pursuit of mechanism of action: uncovering drug complexity in TB drug discovery. RSC chemical biology. 2021;2(2):423\u201340. https:\/\/doi.org\/10.1039\/d0cb00226g.","journal-title":"RSC chemical biology"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Trapotsi M-A, Barrett I, Engkvist O, Bender A. Bioinformatic approaches in the understanding of mechanism of action (MoA). In: Plowright AT, editor. Target discovery and validation. https:\/\/doi.org\/10.1002\/9783527818242.ch11.","DOI":"10.1002\/9783527818242.ch11"},{"issue":"12","key":"12_CR6","doi-asserted-by":"publisher","first-page":"609","DOI":"10.3390\/md18120609","volume":"18","author":"SA Dyshlovoy","year":"2020","unstructured":"Dyshlovoy SA, et al. Efficacy and mechanism of action of marine alkaloid 3,10-dibromofascaplysin in drug-resistant prostate cancer cells. Mar Drugs. 2020;18(12):609. https:\/\/doi.org\/10.3390\/md18120609.","journal-title":"Mar Drugs"},{"issue":"5\u20136","key":"12_CR7","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/s0049-3848(03)00379-7","volume":"110","author":"JR Vane","year":"2003","unstructured":"Vane JR, Botting RM. The mechanism of action of aspirin. Thromb Res. 2003;110(5\u20136):255\u20138. https:\/\/doi.org\/10.1016\/s0049-3848(03)00379-7.","journal-title":"Thromb Res"},{"issue":"10","key":"12_CR8","doi-asserted-by":"publisher","first-page":"655","DOI":"10.3390\/toxins12100655","volume":"12","author":"Y Li","year":"2020","unstructured":"Li Y, et al. Research on the mechanism of action of a citrinin and anti-citrinin antibody based on mimotope X27. Toxins. 2020;12(10):655. https:\/\/doi.org\/10.3390\/toxins12100655.","journal-title":"Toxins"},{"issue":"1","key":"12_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1039\/b515328j","volume":"2","author":"X Fang","year":"2006","unstructured":"Fang X, et al. The mechanism of action of ramoplanin and enduracidin. Mol bioSystems. 2006;2(1):69\u201376. https:\/\/doi.org\/10.1039\/b515328j.","journal-title":"Mol bioSystems."},{"issue":"6400","key":"12_CR10","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1136\/bmj.287.6400.1199","volume":"287","author":"L Krause","year":"1983","unstructured":"Krause L, Shuster S. Mechanism of action of antipruritic drugs. Br Med J Clin Res Ed. 1983;287(6400):1199\u2013200. https:\/\/doi.org\/10.1136\/bmj.287.6400.1199.","journal-title":"Br Med J Clin Res Ed"},{"issue":"24","key":"12_CR11","doi-asserted-by":"publisher","first-page":"9532","DOI":"10.3390\/ijms21249532","volume":"21","author":"D Grinchii","year":"2020","unstructured":"Grinchii D, Eliyahu D. Mechanism of action of atypical antipsychotic drugs in mood disorders. Int J Mol Sci. 2020;21(24):9532. https:\/\/doi.org\/10.3390\/ijms21249532.","journal-title":"Int J Mol Sci"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Koranne, S. Hierarchical data format 5: HDF5. In: Handbook of open source tools. Springer, Boston, MA, 2011. p. 191\u2013200. https:\/\/doi.org\/10.1007\/978-1-4419-7719-9_10.","DOI":"10.1007\/978-1-4419-7719-9_10"},{"issue":"1","key":"12_CR13","doi-asserted-by":"publisher","first-page":"01204","DOI":"10.1088\/1742-6596\/1914\/1\/012034","volume":"1914","author":"GR Puneeth","year":"2021","unstructured":"Puneeth GR, et al. Analysis of drug classification using mechanism of action. J Phys Conf Ser. 2021;1914(1):01204. https:\/\/doi.org\/10.1088\/1742-6596\/1914\/1\/012034.","journal-title":"J Phys Conf Ser"},{"key":"12_CR14","unstructured":"Mechanism of Action Dataset, Kaggle, https:\/\/www.kaggle.com\/c\/lish-moa\/data."},{"key":"12_CR15","unstructured":"Evaluation of the model, Kaggle, https:\/\/www.kaggle.com\/c\/lish-moa\/overview\/evaluation."},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Spyromitros E, Tsoumakas G, Vlahavas I. An empirical study of lazy multilabel classification algorithms. In: Darzentas J, Vouros GA, Vosinakis S, Arnellos A. editors, Artificial intelligence: theories, models and applications. SETN 2008. Lecture Notes in Computer Science, vol. 5138. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-87881-0_40.","DOI":"10.1007\/978-3-540-87881-0_40"},{"issue":"7","key":"12_CR17","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"Z Min-Ling","year":"2007","unstructured":"Min-Ling Z, Zhi-Hua Z. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 2007;40(7):2038\u201348. https:\/\/doi.org\/10.1016\/j.patcog.2006.12.019.","journal-title":"Pattern Recogn"},{"key":"12_CR18","unstructured":"Szyma\u0144ski P, Kajdanowicz T. A Scikit-based python environment for performing multi-label classification. 2017, allrXiv:1702.01460. arXiv.org e-Print archive. hps:\/\/arxiv.org\/abs\/1702.01460."},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Ketkar N. Introduction to Keras. In: Deep learning with python. 2017. Apress, Berkeley, CA. https:\/\/doi.org\/10.1007\/978-1-4842-2766-4_7.","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"12_CR20","unstructured":"Chollet F et al. Keras. GitHub. Retrieved from https:\/\/github.com\/fchollet\/keras. 2015."},{"key":"12_CR21","unstructured":"Source code for the MoA web application and the implementation of Neural network, GitHub. Retrieved from https:\/\/github.com\/Puneethgr\/mechanism-of-action."},{"key":"12_CR22","unstructured":"Flask Web Framework, Official Documentation, https:\/\/flask.palletsprojects.com\/."},{"key":"12_CR23","unstructured":"Jinga Template, Official Documentation, https:\/\/jinja.palletsprojects.com\/."}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-021-00012-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-021-00012-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-021-00012-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T13:20:09Z","timestamp":1637760009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-021-00012-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["12"],"URL":"https:\/\/doi.org\/10.1007\/s44163-021-00012-2","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]},"assertion":[{"value":"15 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13"}}