{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T15:47:26Z","timestamp":1763480846622,"version":"build-2065373602"},"reference-count":48,"publisher":"Wiley","issue":"21","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["chemistry-europe.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ChemistrySelect"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Leishmaniasis is a neglected tropical disease impacting over 1 billion people worldwide. Existing treatments have limitations in toxicity and efficacy, prompting the need for new therapies. This study combines machine learning (ML), quantitative structure\u2010activity relationship (QSAR), and polypharmacology to identify potential drug candidates from natural sources and repurpose existing drugs. A dataset of 12,072 compounds with IC50 values for nine\n                    <jats:italic>Leishmania<\/jats:italic>\n                    species, described by PubChem fingerprints, was analyzed. Using the random forest and XGBoost models, the study achieved predictive accuracy with R\n                    <jats:sup>2<\/jats:sup>\n                    scores of 0.70\u20100.71 and RMSE metrics around 0.46\u20100.47. Key findings include natural products from worldwide biodiversity, such as 16\u2010acetoxy\u201011\u2010hydroxyoctadeca\u201017\u2010ene\u201012,14\u2010diynyl acetate, 12\u2010methyl\u2010E,E\u20102,13\u2010octadecadien\u20101\u2010ol, nonadec\u20105\u2010en\u20101\u2010ol, and dihydrocyclobuxine D. Drug repurposing efforts highlighted significant antileishmanial activity with nystatin, stamycin, spiramycin, solamargine, and plicamycin. This approach highlights ML's potential in accelerating leishmaniasis treatment discovery.\n                  <\/jats:p>","DOI":"10.1002\/slct.202501979","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T05:42:34Z","timestamp":1748410954000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integration of Machine Learning\u2010QSAR and Polypharmacology in Drug Discovery for Leishmaniasis Using Natural and Repurposed Compounds"],"prefix":"10.1002","volume":"10","author":[{"given":"La\u00eds","family":"Salvador do Amaral","sequence":"first","affiliation":[{"name":"Postgraduate Programme in Pharmaceutical Sciences Federal University of Paran\u00e1  Av. Pref. Loth\u00e1rio Meissner, 632 Curitiba Paran\u00e1 80210\u2010170 Brazil"}]},{"given":"Amiel Artur","family":"Morais","sequence":"additional","affiliation":[{"name":"Postgraduate Programme in Pharmaceutical Sciences Federal University of Paran\u00e1  Av. Pref. Loth\u00e1rio Meissner, 632 Curitiba Paran\u00e1 80210\u2010170 Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6642-3928","authenticated-orcid":false,"given":"Alexandre de F\u00e1tima","family":"Cobre","sequence":"additional","affiliation":[{"name":"Postgraduate Programme in Pharmaceutical Sciences Federal University of Paran\u00e1  Av. Pref. Loth\u00e1rio Meissner, 632 Curitiba Paran\u00e1 80210\u2010170 Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3434-1239","authenticated-orcid":false,"given":"Raul Edison Luna","family":"Lazo","sequence":"additional","affiliation":[{"name":"Postgraduate Programme in Pharmaceutical Sciences Federal University of Paran\u00e1  Av. Pref. 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