Antimicrobial and Antiparasitic Drug Discovery: Experimental Pharmacology, Computational and AI/ML Approaches, and Translational Strategies

Antimicrobial resistance is one of the defining health threats of our time. Drug-resistant bacteria, fungi, and parasites now undermine treatments that once worked reliably, and the pipeline of new therapeutic agents has not kept pace. Many pathogens that cause tuberculosis, malaria, leishmaniasis, and other infections have developed resistance to frontline drugs, while the commercial and scientific barriers to developing new anti-infectives remain high. Closing this gap requires fresh chemical matter, validated molecular targets, and faster, smarter ways to move a hit toward a candidate.Progress increasingly depends on combining disciplines. Structure-based and computer-aided drug design, machine learning, and virtual screening can prioritize promising scaffolds and predict activity, selectivity, and liabilities early. But computational predictions only become drug discovery when they are tested at the bench. The most productive programs pair in silico design with medicinal chemistry, target-based and phenotypic assays, mechanism-of-action studies, and pharmacokinetic and toxicological profiling. This Research Topic focuses on that integrated cycle, where computational and AI/ML methods guide experimental pharmacology, and experimental results refine the models in turn.We welcome contributions that advance the discovery, design, synthesis, and mechanistic characterization of new antimicrobial and antiparasitic agents. Studies should provide detailed, in-depth insight into the interaction between compounds and specific biological targets, such as enzymes, receptors, transporters, and other validated molecular targets, and the pharmacological consequences of those interactions.Themes of interest include, but are not limited to:Rational and structure-based design of novel antibacterial, antifungal, antimycobacterial, antimalarial, and antiparasitic agentsMedicinal chemistry campaigns: hit identification, lead optimization case studies, parallel synthesis, and library designIdentification and validation of new molecular targets (for example, essential enzymes in fatty acid biosynthesis or isoprenoid pathways) using high-throughput screening and lead-finding techniquesComputer-aided drug design, molecular modeling, machine learning, and AI/ML models for hit prioritization, activity and ADMET prediction, coupled with experimental confirmationElucidation of mechanisms of action, binding and transduction mechanisms, and drug–biomolecule interactionsPharmacokinetics, metabolism, and toxicology of investigational anti-infective compoundsStrategies to overcome resistance, including new chemotypes, target-based approaches, and adjuvants that restore drug activityTranslational strategies that move validated chemical matter toward preclinical developmentTo remain within the section’s scope, all submissions must have a clear experimental pharmacology or drug discovery component. Purely in silico studies, work on crude extracts or multiherbal preparations, and clinical trials fall outside the scope of this section. Where computational methods are central, they should be accompanied by experimental validation. Original Research, Reviews, Mini Reviews, Methods, Hypothesis and Theory, and other article types accepted by the section are welcome.By bringing together experimental pharmacologists, medicinal chemists, and computational and data scientists, this Research Topic aims to accelerate the discovery of next-generation anti-infective therapeutics and to strengthen the bridge between computational prediction and validated pharmacology. This collection supports SDG 3: Good Health and Well-being. Topic editors: Sandeep Sundriyal, Shruthi Sridhar Vembar, Karunakaran Kalesh Source: https://www.frontiersin.org/research-topics/84162/antimicrobial-and-antiparasitic-drug-discovery-experimental-pharmacology-computational-and-aiml-approaches-and-translational-strategies