Abstract – Publication

Multiscale-aware graph embedding approach uncovers LC-61, a potent anti-leishmania infantum compound.
COSTA, Vinícius Alexandre Fiaia; CARVALHO, Alexandra Maria do Santos; CHELUCCI, Rafael Consolin; MELO, Felipe da Silva Mendonça de; FELIZARDO, Gustavo Santos Sandes; BERNARDES, Clarissa Alves Carneiro; MARTIN, Holli-Joi; BRAGA, Rodolpho de Campos; CHARNEAU, Sébastien; MURATOV, Eugene N.; ANDRICOPULO, Adriano Defini; BASTOS, Izabela Marques Dourado; NEVES, Bruno Junior.
Abstract: Visceral leishmaniasis caused by Leishmania infantum remains a lethal disease with few therapeutic options, necessitating innovative computational methods and approaches to accelerate drug discovery. Here, we present a graph neural network (GNN) framework incorporating well-established multiscale mechanisms to improve the identification of novel antileishmanial compounds. Across two classificatory antileishmanial data sets, our GNNs demonstrated significant improvements in predictive performance, with area under the receiver operating characteristic curve (AUC) increases of 2.2-29.2% on the imbalanced data set (activity cutoff: 1 µM) and 3.4-22.5% on the balanced data set (activity cutoff: 10 µM) compared to default GNNs. Subsequently, the framework was applied to screen a library of approximately 1.3 million compounds, pinpointing LC-61 as a potent antileishmanial agent with nanomolar activity against intracellular L. infantum (IC50 = 0.076 µM) and minimal cytotoxicity to macrophages (THP-1 CC50 = 157 µM). A comprehensive in vitro ADME profiling revealed that LC-61 combines high solubility at both acidic and physiological pH (>28 µg/mL), balanced lipophilicity (eLogD = 4.07), and favorable passive permeability (PAMPA = 4.86 × 10-6 cm/s), while exhibiting lower microsomal stability. Overall, our GNN framework effectively accelerated the discovery of LC-61, a novel and biologically validated hit suitable for hit-to-lead optimization.
Journal of Chemical Information and Modeling
v. 66, n. 7, p. 3643-3659 + supporting information - Ano: 2026
Fator de Impacto: 5,3
    @article={003299186,author = {COSTA, Vinícius Alexandre Fiaia; CARVALHO, Alexandra Maria do Santos; CHELUCCI, Rafael Consolin; MELO, Felipe da Silva Mendonça de; FELIZARDO, Gustavo Santos Sandes; BERNARDES, Clarissa Alves Carneiro; MARTIN, Holli-Joi; BRAGA, Rodolpho de Campos; CHARNEAU, Sébastien; MURATOV, Eugene N.; ANDRICOPULO, Adriano Defini; BASTOS, Izabela Marques Dourado; NEVES, Bruno Junior.},title={Multiscale-aware graph embedding approach uncovers LC-61, a potent anti-leishmania infantum compound},journal={Journal of Chemical Information and Modeling},note={v. 66, n. 7, p. 3643-3659 + supporting information},year={2026}}