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

Clinical Snapshot

Key Findings

• P Value: Not specified

• Effect Size: Selectivity index >2000 (CC50 157 μM / IC50 0.076 μM)

• Primary Outcome: LC-61 demonstrated nanomolar activity against intracellular L. infantum with IC50 = 0.076 μM

• Nnt Or Sensitivity: AUC improvements of 2.2-29.2% over default GNNs on imbalanced dataset

• Confidence Interval: Not provided

Clinical Application

Early-stage discovery compound requiring further optimization and clinical development before therapeutic application Relevant for Australian travelers to endemic regions and immigrant populations. TGA approval would be required for clinical development. Limited current PBS coverage for leishmaniasis treatments Patients with visceral leishmaniasis, particularly those with drug-resistant infections or contraindications to current therapies

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.

References

1. 1.Costa, V. A., Carvalho, A. M. S., Chelucci, R. C., Mendonça de Melo, F. S., Felizardo, G. S. S., Bernardes, C. A. C., Martin, H.-J., Braga, R. C., Charneau, S., Muratov, E. N., Andricopulo, A. D., Bastos, I. M. D., & Neves, B. J. (2026). Multiscale-aware graph embedding approach uncovers LC-61, a potent anti-Leishmania infantum compound. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.5c02947