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

Visceral leishmaniasis caused by <i>Leishmania infantum</i> 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 <i>L. infantum</i> (IC₅₀ = 0.076 μM) and minimal cytotoxicity to macrophages (THP-1 CC₅₀ = 157 μM). A comprehensive <i>in vitro</i> 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.