@article{103964,
  keywords = {Artificial Intelligence, Leishmaniasis, Drug Discovery, Machine learning, Virtual screening, Quantitative structure–activity relationship},
  author = {Blaskovski NR and Fanhani FC and Scheiffer GH and Rondan MAM and Lazo REL and Hammerschmitt BK and Sari MHM and Pontarolo R and Ferreira LM},
  title = {How is artificial intelligence being used to discover new treatments for leishmaniasis? A Scoping Review},
  abstract = {<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>
},
  year = {2026},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  volume = {278},
  pages = {1 - 17},
  month = {08/2026},
  publisher = {Elsevier BV},
  issn = {0169-7439},
  url = {https://www.sciencedirect.com/science/article/pii/S0169743926002273/pdfft?md5=47f2816e60daac4dd0c151dd35fabe27&pid=1-s2.0-S0169743926002273-main.pdf},
  doi = {10.1016/j.chemolab.2026.105854},
  language = {ENG},
}