Multiscale Computational Pharmacology for Infectious Diseases: Integrating Systems, Network, Simulation, and AI-Driven Drug Discovery

This Research Topic seeks to highlight advancements in systems-based pharmacology, network-based pharmacology, molecular docking methodologies, molecular dynamics simulations (including free energy computations), and machine learning models for drug discovery and ADMET predictions. Special emphasis will be placed on studies targeting infectious diseases, including applications of computational strategies to identify, optimize, and evaluate anti-infective agents. Systems-based pharmacology applies network analyses and multiscale computational modeling to predict therapeutic efficacy and adverse effects of drugs. Network-based pharmacology leverages graph-theoretic frameworks of drug-target and disease networks to identify synergistic drug combinations and opportunities for drug repurposing. Molecular docking methodologies enable high-throughput virtual screening of extensive compound libraries and accurate prediction of ligand-receptor binding poses. Molecular dynamics simulations offer atomistic insights into the conformational dynamics of biomolecular complexes, validate docking predictions, and facilitate free energy calculations for binding affinity estimations. Machine learning models, ranging from deep neural networks to graph-based algorithms, are revolutionizing drug discovery pipelines by providing rapid, scalable predictions of ADMET properties and binding affinities. Studies that combine two or more of these computational strategies to deliver integrative, mechanistic insights into drug-target interactions, optimize lead compounds, and accelerate translational research in infectious diseases are especially welcome. In line with our editorial policies, manuscripts primarily comprising of bioinformatics analyses, molecular docking and structure predictions, molecular dynamics simulations, computational studies such as those involving AI, or findings from Mendelian Randomization studies must include appropriate validation. Acceptable forms of validation include independent clinical or patient cohorts or biological validation, either in vitro or in vivo

This Research Topic aims to foster cutting-edge research focused on the development and mechanistic analysis of therapies for infectious diseases using multiscale computational methods. Systems- based pharmacology, network-based pharmacology, molecular docking, molecular dynamics simulations, and machine-learning approaches for drug discovery and ADMET predictions are encouraged.

We welcome original research articles, reviews, and perspectives that demonstrate:

• Interdisciplinary methodologies bridging multiple computational modalities for anti-infective drug discovery.
• Translational applications with experimental or clinical validation.
• Open-source tools, databases, and reproducible workflows.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: computational pharmacology, AI, infectious diseases, simulation, drug discovery

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.