From genomes to interventions: computational strategies transforming parasitology

In silico techniques have become essential in contemporary parasitology, offering swift, cost-effective, and scalable methods for exploring parasite biology, host-parasite interactions, drug resistance, the discovery of diagnostic markers, vaccine development, and the prioritization of therapeutic targets. Computational frameworks that encompass genomics, pangenomics, phylogenetics, transcriptomics, proteomics, structural biology, molecular docking, molecular dynamics simulations, artificial intelligence (AI), and immunoinformatics have collectively revolutionized parasite research. They enable the systematic identification of conserved antigens, lineage-specific virulence factors, metabolic weaknesses, and potential therapeutics across the primary parasitic disease categories discussed in this review. Simultaneously, the growth of databases and analytical platforms focused on parasites has enhanced genome annotation, inhibitor discovery, epitope prediction, and systems-level analysis. However, despite these advancements, numerous workflows rely on incomplete or inadequately annotated datasets and biologically oversimplified assumptions that fail to accurately represent the complexity of parasites, variations in their life cycles, and host-dependent factors-further complicated by inconsistent data quality and diminished reproducibility across species. As a result, computational findings necessitate thorough integration with transcriptomic, proteomic, structural, and functional experimental data. When utilized in this collaborative manner, in silico methods expedite hypothesis generation, refine experimental parameters, and bolster rational approaches for drug discovery, vaccine design, and epidemiological surveillance.