03484nas a2200313   4500000000100000008004100001260002500042653002800067653001800095653001900113653002100132653002200153653005100175100001800226700001500244700001700259700001500276700001300291700002100304700001300325700001600338700001600354245010900370856015300479300001100632490000800643520250500651022001403156       2026                            d  c08/2026bElsevier BV10aArtificial Intelligence10aLeishmaniasis10aDrug Discovery10aMachine learning10aVirtual screening10aQuantitative structure–activity relationship1 aBlaskovski NR1 aFanhani FC1 aScheiffer GH1 aRondan MAM1 aLazo REL1 aHammerschmitt BK1 aSari MHM1 aPontarolo R1 aFerreira LM00aHow is artificial intelligence being used to discover new treatments for leishmaniasis? A Scoping Review  uhttps://www.sciencedirect.com/science/article/pii/S0169743926002273/pdfft?md5=47f2816e60daac4dd0c151dd35fabe27&pid=1-s2.0-S0169743926002273-main.pdf  a1 - 170 v2783 a<p>Leishmaniasis remains a major neglected tropical disease, and the discovery of new therapeutic options is hindered by high costs, long development timelines, and the parasite's biological complexity. In this context, artificial intelligence has emerged as a promising strategy to accelerate and refine different stages of the drug discovery process. This scoping review aimed to map how artificial intelligence has been applied in the discovery of new treatments for leishmaniasis, identifying the main computational approaches, their applications, the validation strategies employed (including cross-validation and external testing), and the translational outputs reported in the literature. The review was conducted in accordance with the Joanna Briggs Institute recommendations, and studies were retrieved from PubMed, Scopus, and Web of Science using predefined eligibility criteria. The data report was prepared in accordance with the PRISMA-ScR checklist. The included studies showed that artificial intelligence has been applied across multiple stages of antileishmanial drug discovery, including target identification and validation, ligand-based and structure-based virtual screening, hit discovery, lead optimization, and pharmacokinetic and toxicity prediction. Random Forest was the most frequently reported model, particularly in ligand-based virtual screening and quantitative structure–activity relationship modeling, whereas Support Vector Machine, Decision Tree, Artificial Neural Networks, clustering approaches, and deep learning tools such as AlphaFold, DeepLoc, DeepPurpose, and MONN were also identified. The most common applications involved structural relationship prediction, especially binary structure–activity relationship classification, followed by pattern recognition and screening, biological identification and characterization, and descriptor selection. Overall, the findings indicate that artificial intelligence has become a versatile and increasingly relevant tool in antileishmanial drug discovery. However, important limitations remain, including data scarcity, dataset imbalance (32.7% of the included studies did not report applying to a class-imbalance-handling procedure), a lack of parasite-specific benchmarks, and the predominance of studies restricted to in silico validation. Even so, integrating artificial intelligence with experimental validation may substantially strengthen future efforts to identify new treatments for leishmaniasis.</p>
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