The incidence of fungal infections continues to increase and one of the factors responsible for these high rates is the emergence of multi-resistant species, hospitalizations, inappropriate or prolonged use of medications, and pandemics, such as the ongoing HIV/AIDS pandemic. The recent pandemic caused by the severe acute respiratory syndrome virus (SARS-CoV-2) has led to a significant increase in fungal infections, especially systemic mycoses caused by opportunistic fungi. There is a growing and urgent need to better understand how these microorganisms cause infection and develop resistance as well as to develop new therapeutic strategies to combat the diverse diseases caused by fungi. Non-mammalian hosts are increasingly used as alternative models to study microbial infections. Due to their low cost, simplicity of care, conserved innate immunity and reduced ethical issues, the greater wax moth Galleria mellonella is an excellent model host for studying fungal infections and it is currently widely used to study fungal pathogenesis and develop innovative strategies to mitigate the mycoses studied. G. mellonella can grow at 37 °C, which is similar to the mammalian temperature, and the anatomy of the larvae allows researchers to easily deliver pathogens, biological products, compounds and drugs. The aim of this review is to describe how G. mellonella is being used as a model system to study fungal infections as well as the importance of this model in evaluating the antifungal profile of potential drug candidates or new therapies against fungi.

Background: Leishmania amazonensis, one of the causative agents of cutaneous leishmaniasis, is responsible for a neglected tropical disease affecting nearly one million individuals worldwide. Although clinical treatments are available, their effectiveness is often compromised by high toxicity and limited selectivity. Methods: From a database, 64 chalcone derivatives were studied using Comparative Molecular Similarity Indices Analysis (CoMSIA) and Hansch analysis (2D-QSAR) to construct predictive computational models. Internal and external validation criteria were applied to identify structural determinants associated with antileishmanial activity. Based on these insights, twelve novel quinoline–chalcone hybrids were designed, synthesized, and biologically evaluated against L. amazonensis. Results: The most robust CoMSIA model combined steric and hydrogen-bond acceptor fields (CoMSIA-SA), while Hansch analysis highlighted electronic descriptors—specifically LUMO energy and the global electrophilicity index—as critical for parasite growth inhibition. Guided by these computational findings, a new series of 12 hybrid quinoline–chalcone derivatives (E001–E012) was synthesized through a two-step procedure, achieving overall yields of 43–71%. Biological assays demonstrated that four compounds displayed inhibitory activity comparable to amphotericin B. Furthermore, physicochemical profiling and in silico pharmacokinetic predictions for the most active compounds (E003, E005, E006, and E011) indicated favorable biocompatibility and drug-like properties. Conclusions: These results underscore the value of an integrative computational–experimental approach in the rational design of next-generation L. amazonensis inhibitors, reinforcing chalcone-based scaffolds as promising pharmacophoric frameworks for antileishmanial drug discovery.