Computational Frontiers in Anti-Protist Drug Discovery

Protist pathogens, including genera such as Plasmodium, Leishmania, and Trypanosoma, remain a major and often overlooked global health burden, particularly in low- and middle-income regions. Despite increasing research efforts, current therapeutic options remain limited and are frequently associated with toxicity, poor selectivity, and the rapid emergence of drug resistance. Traditional drug discovery pipelines are time-consuming and resource-intensive, underscoring the urgent need for innovative and accelerated strategies. Recent advances in computational biology, artificial intelligence, and cheminformatics have opened new avenues for identifying and optimizing anti-protist agents. In parallel, expanding chemical modalities—including small molecules, peptides, and peptidomimetics—offer promising opportunities to target previously intractable biological pathways and macromolecular interactions within these pathogens.This special issue aims to highlight cutting-edge advances at the intersection of computational methods, chemistry, and biology in anti-protist drug discovery. The goal is to provide a platform for interdisciplinary research that leverages in silico methods to accelerate the identification, design, and optimization of therapeutic candidates. We seek contributions that demonstrate how computational tools—ranging from molecular modeling and virtual screening to machine learning—can be effectively integrated with experimental validation to generate impactful insights. Emphasis is placed on innovative strategies that address key challenges such as drug resistance, selectivity, and target identification. By bringing together diverse approaches, this issue aims to foster collaboration and advance the development of next-generation therapeutics against protist diseases.We invite original research articles, reviews, and methodological studies that explore computational and chemical approaches to advance anti-protist drug discovery. Topics of interest include, but are not limited to: structure- and ligand-based drug design; molecular docking and molecular dynamics simulations; AI- and machine learning-driven discovery; virtual and high-throughput screening; and free energy calculations. Contributions focusing on small molecules, peptides and peptidomimetics with anti-protist activity, are particularly encouraged. Studies addressing drug targets such as enzymes, RNA–protein interactions, protein–protein interactions, and host–pathogen interfaces are welcome. Submissions integrating omics data, network pharmacology, or multi-target strategies are also within scope. Both purely computational and integrated computational–experimental workflows are suitable, particularly those demonstrating translational relevance or therapeutic potential. Topic editors: Saurabh Loharch, Gurudutt Dubey, Violeta Markovic, Monika Szefczyk Source: https://www.frontiersin.org/research-topics/81282/computational-frontiers-in-anti-protist-drug-discovery