AI-Driven Molecular Modeling for Next-Generation Therapeutics Against Emerging Infectious Diseases

Emerging infectious diseases (EIDs) that cause recurrent outbreaks continuously threaten public health due to factors like global connectivity, ecological disturbances, and the evolution of pathogens. Most EIDs originate from zoonotic agents and can be transmitted via food, vectors, or airborne routes. Key drivers of EID emergence include population growth, aging demographics, international travel, transmission in healthcare environments, and climate change altering vector habitats. Examples of such diseases include Mpox (Clade I and II), coronaviruses (COVID-19, SARS, MERS), HIV, Lyme disease, Escherichia coli O157:H7, Hantavirus, Dengue fever, West Nile virus, and Zika virus. These viral and bacterial infections rapidly spread through community transmission and travel-related cases, often involving variants and recombination events, causing significant public health, societal, and economic challenges. Their complex interplay among human, animal, and environmental health complicates early detection and prevention efforts.

Although some therapeutics, vaccines, and emergency treatments exist, there remains an urgent demand for more effective therapies against EIDs. The design of such therapeutics is therefore a critical priority. Advances in machine learning (ML), artificial intelligence (AI), and molecular modeling hold great promise for expediting the screening and development of small and large molecules targeting essential drug targets within EIDs.

These emerging research topics invite studies investigating the mechanisms and roles of therapeutic targets for EIDs, including the screening of micro- and macromolecules using integrated AI, ML and molecular modeling techniques. We also welcome research on medicinal plant derivatives as potential treatments. Additionally, comprehensive reviews addressing the application of machine learning and molecular modeling in drug discovery for EID management are highly encouraged.

Areas to be covered in this Research Topic may include, but are not limited to:
• AI-driven virtual screening and repurposing strategies using integrated molecular modeling.
• Structure-based modeling to identify EID functional and druggable residues.
• AI approach for identifying novel molecules for drug repurposing.
• Exploration of medicinal plant-derived compounds for therapeutic applications.
• Antibody-pathogen protein interactions for therapeutic insights.


Anuj Kumar is a recipient of Moderna Global Fellowship. The other Topic Editors declare no competing interests with regard to the Research Topic subject.

Article types and fees

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

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Mini Review
• Original Research
• Perspective
• Review

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Article types

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

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Mini Review
• Original Research
• Perspective
• Review

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.