ArticlemicroPublication biology2026
Predicting Antileishmanial Activity of Plant-Derived Compounds Using Random Forest Modeling.
Article in microPublication biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leishmaniasis is a parasitic disease for which existing treatments can be toxic and costly, and resistance to current therapies is becoming increasingly common. This study implemented a systematic review of data regarding antileishmanial activity in plant-derived compounds and used machine learning techniques to train and test a Random Forest algorithm to predict the antileishmanial activity of plant-derived compounds. Asteraceae, Euphorbiaceae, Lamiaceae, and Myrtaceae plant families were identified for their high or moderate antileishmanial activity and nativity to areas of high leishmaniasis prevalence. The Random Forest model had 89% prediction accuracy, with an out-of-bag error rate of 16%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.