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Leishmaniasis

Leishmaniasis

Leishmaniasis is a protozoal disease caused by species of the genus Leishmania and transmitted through the bite of infected female sandflies. The parasite exists in two principal morphological forms during its life cycle. A sandfly acquires intracellular amastigotes from an infected reservoir host, such as a dog. Within the sandfly, these forms differentiate into motile, flagellated promastigotes. During a subsequent blood meal, promastigotes are injected into the human host, where they...

Antiprotozoal Agents

Antiprotozoal Agents

Leishmaniasis is a widespread parasitic disease caused by several Leishmania species. It affects millions of people each year and remains a major public health problem in endemic regions. First-line treatment relies on pentavalent antimonials, including meglumine antimoniate and sodium stibogluconate. Even so, how these drugs work has not been fully clear, especially their interaction with parasite-specific biochemical pathways. One key target is trypanothione reductase (TR), an enzyme that...

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Related Experiment Video

Updated: Jun 14, 2026

An Efficient Method for the Synthesis of Peptoids with Mixed Lysine-type/Arginine-type Monomers and Evaluation of Their Anti-leishmanial Activity

An Efficient Method for the Synthesis of Peptoids with Mixed Lysine-type/Arginine-type Monomers and Evaluation of Their Anti-leishmanial Activity

Published on: November 2, 2016

Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Leishmania infantum Compound.

Vinícius Alexandre Fiaia Costa¹, Alexandra Maria Dos Santos Carvalho², Rafael Consolin Chelucci³

¹Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, 74605-170 Goiânia, Brazil.

Journal of Chemical Information and Modeling

|March 17, 2026

Summary

This summary is machine-generated.

This study introduces a graph neural network (GNN) framework for discovering new drugs against visceral leishmaniasis. The GNN approach successfully identified a potent antileishmanial compound, LC-61, with favorable properties for further development.

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Area of Science:

Computational chemistry
Drug discovery
Parasitology

Background:

Visceral leishmaniasis, caused by *Leishmania infantum*, is a fatal disease with limited treatment options.
Accelerating the discovery of novel antileishmanial compounds is crucial.

Purpose of the Study:

To develop and validate a graph neural network (GNN) framework for identifying novel antileishmanial drug candidates.
To screen a large compound library and identify promising lead compounds.

Main Methods:

Implementation of a GNN framework integrating multiscale mechanisms for enhanced predictive performance.
Evaluation of GNNs on two antileishmanial datasets, showing significant AUC improvements.
Screening of approximately 1.3 million compounds to identify potential drug candidates.

Main Results:

The GNN framework demonstrated substantial improvements in predictive accuracy compared to default GNNs.
Identified LC-61 as a potent antileishmanial agent with nanomolar activity (IC50 = 0.076 μM) and low cytotoxicity (CC50 = 157 μM).
LC-61 exhibited favorable *in vitro* ADME properties, including high solubility and balanced lipophilicity.

Conclusions:

The GNN framework effectively accelerated the identification of novel antileishmanial compounds.
LC-61 is a validated hit compound with promising characteristics for hit-to-lead optimization in visceral leishmaniasis drug discovery.