A Decade of In Silico Drug Discovery for Naegleria fowleri: Progress, Challenges, and Future Directions

Abstract

The brain-eating amoeba, more formally known as Naegleria fowleri (N. fowleri), is the main cause of Primary Amoebic Meningoencephalitis (PAM), a rare albeit universally fatal infection. The rapid infectivity in conjunction with its guaranteed lethality alongside its limited treatment options should be the proverbial klaxon that signals for new therapeutic techniques. In recent years, in silico drug discovery, which includes computational techniques like molecular docking, molecular dynamics simulations, pharmacophore modelling, virtual screening, and QSAR analysis, has emerged as a powerful tool to identify potential treatments for PAM. Researchers have expended no small number of cognitive resources in fully maximising these methods to identify promising drug candidates, including both novel molecules and repurposed FDA-approved drugs. These efforts target essential proteins and metabolic pathways in N. fowleri, with structural bioinformatics helping to identify key drug targets such as proteases, kinases, and metabolic enzymes. The integration of AI and machine learning has further optimised the drug discovery process, enhancing prediction accuracy and compound optimisation. Additionally, diving into green chemistry with the goal of minimising toxicity and environmental impact. However, challenges remain. These include a shortfall of detailed protein structures, limited experimental validation, gaps between computational predictions, and clinical effectiveness. Progressing forward, researchers are encouraged to integrate multi-omics data, expand screening libraries, and improve predictive models to accelerate the discovery of eco-friendly and effective therapies. Combining bioinformatics, chemistry, and experimental biology is paramount for translating. In accordance with PRISMA 2020 guidelines, this study reviews in silico studies published between 2015 and 2025 using search engines like PubMed and Google Scholar. A multidisciplinary approach combining bioinformatics, chemistry, and experimental biology is paramount to translate computational leads into clinically viable treatments and experimental workflows for neglected protozoan diseases.