Emmanuel Nkansah¹, Micheal Abimbola Oladosu²*, Moses Adondua Abah³, Abimbola Mary Oluwajembola², Fwangmun Ezekiel Gushit⁴, Olaide Ayokunmi Oladosu⁵, Adesola Esther Adeneye⁶ and Bukola Oluwaseyi Olufosoye⁷

¹Department of Accounting, Economics and Finance, School of Business, La Sierra
University, Riverside, CA, USA
²Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo,
Ipaja, Lagos, Nigeria
³Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal
University of Wukari, Wukari, Taraba State, Nigeria
⁴Department of Public Health, Faculty of Health Science, Ahmadu Bello University,
Zaria, Kaduna State, Nigeria
⁵Department of Computer Science, Faculty of Science and Technology, Babcock
University, Ilishan, Nigeria
⁶Department of Biological Sciences, Faculty of Science, Anchor University, Ayobo,
Ipaja, Lagos, Nigeria
⁷Department of Medical Microbiology, Faculty of Medical Laboratory Sciences,
Ambrose Alli University, Ekpoma, Edo State, Nigeria

*Corresponding Author: Micheal Abimbola Oladosu, Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria.

Received: January 21, 2026; Published: March 31, 2026

Abstract

The emergence of artificial intelligence (AI) and advanced computational methods has revolutionised our understanding of host– pathogen interactions in infectious diseases. This review comprehensively examines recent developments in computational modelling, machine learning algorithms, and AI-based simulation techniques applied to predicting and analysing molecular interactions between pathogens and their hosts. We discuss the integration of multi-omics data, protein–protein interaction prediction models, molecular dynamics simulations, and systems biology approaches that collectively enhance our capacity to identify therapeutic targets and understand infection mechanisms. Despite remarkable progress, challenges remain in data quality, model interpretability, and computational resource requirements. The synergistic application of AI with traditional experimental methods offers unprecedented opportunities for accelerating drug discovery, vaccine development, and precision medicine approaches against infectious diseases. This review highlights key methodologies, recent breakthroughs, and future directions in computational host–pathogen interaction research.

Keywords: Artificial Intelligence; Machine Learning; Host-Pathogen Interactions; Protein-Protein Interactions; Computational Modelling; Drug Discovery; Systems Biology; Molecular Dynamics

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Citation

Citation: Micheal Abimbola Oladosu., et al. “Computational Modelling and AI-Based Simulation of Host–Pathogen Interactions in Infectious Diseases". Acta Scientific Microbiology 9.4 (2026): 41-51.

Copyright

Copyright: © 2026 Micheal Abimbola Oladosu., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.