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Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred\u00a9), to predict potential synergy in antimalarial drug combinations.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The MLSyPred\u00a9 synergy prediction method extracts molecular fingerprints from the drugs\u2019 biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred\u00a9 tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of <jats:italic>Plasmodium falciparum<\/jats:italic> (3D7, HB3, and Dd2).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred\u00a9 tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>\u00a0The MLSyPred\u00a9 tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11686-023-00765-z","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T16:03:00Z","timestamp":1704211380000},"page":"415-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred\u00a9) tool"],"prefix":"10.1007","volume":"69","author":[{"given":"Abiel","family":"Roche-Lima","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ang\u00e9lica M.","family":"Rosado-Qui\u00f1ones","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto A.","family":"Feliu-Maldonado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda Del Mar","family":"Figueroa-Gispert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"D\u00edaz-Rivera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto G.","family":"D\u00edaz-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelvin","family":"Carrasquillo-Carrion","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenda G.","family":"Nieves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilee E.","family":"Col\u00f3n-Lorenzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adelfa E.","family":"Serrano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"765_CR1","unstructured":"World Health Organization (2021) World malaria report 2021. 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Biometrics 74:378\u2013384. https:\/\/doi.org\/10.1111\/biom.12855","journal-title":"Biometrics"}],"container-title":["Acta Parasitologica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11686-023-00765-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11686-023-00765-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11686-023-00765-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T14:24:07Z","timestamp":1712586247000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11686-023-00765-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,2]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["765"],"URL":"https:\/\/doi.org\/10.1007\/s11686-023-00765-z","relation":{},"ISSN":["1230-2821","1896-1851"],"issn-type":[{"value":"1230-2821","type":"print"},{"value":"1896-1851","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,2]]},"assertion":[{"value":"21 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This study was conducted using publicly available datasets. The antimalarial datasets included results on drugs and compounds that were freely available to the scientific community that were biologically evaluated by Mott <i>et al<\/i>. [], and tested by Mason <i>et al<\/i>. []. The antibiotic dataset was also freely available to researchers []. The data were used to train and test the machine learning algorithms implemented in the MLSyPred\u00a9 tool. In this research, we did not perform any biological experiments with humans or experimental animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}