Computational Strategies for Antiviral Drug Discovery: QSAR, In Silico Modeling and Data-Driven Approaches
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 182
Special Issue Editor
Interests: rotational and vibrational spectroscopy; molecular reactivity; intermolecular interactions; periodic calculations; SEP generation; metal complexes; photovoltaic cells; docking study; QSAR calculation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Viral diseases continue to pose a major threat to global public health, as highlighted by recurrent outbreaks and emerging viral pathogens. The rapid evolution of viruses and the limited availability of effective antiviral therapies underscore the urgent need for innovative and efficient drug discovery strategies. In this context, computational approaches have emerged as powerful tools to accelerate the identification, optimization, and understanding of antiviral agents.
This Special Issue will showcase recent advances in computational and in silico methodologies applied to antiviral drug discovery, with a particular emphasis on Quantitative Structure–Activity Relationship (QSAR) modeling and related techniques. By integrating chemical, biological, and computational insights, these approaches enable the rational design of bioactive compounds, reduce experimental costs, and improve the success rate of antiviral lead discovery.
This Special Issue covers varied computational strategies, including QSAR modeling, molecular modeling, molecular dynamics simulations, virtual screening, and structure–activity relationship analyses. Special attention is given to the growing role of artificial intelligence and machine learning in predicting antiviral activity, identifying key molecular descriptors, and guiding the optimization of candidate compounds. Contributions that combine experimental validation with computational modeling are particularly encouraged, as they provide robust and translational insights into antiviral drug development.
By bringing together high-quality original research articles and reviews, this Special Issue will foster interdisciplinary collaboration among researchers in chemistry, biology, pharmacology, and the computational sciences. The collected contributions will provide a comprehensive overview of the current challenges and emerging opportunities in computational antiviral research, ultimately contributing to the development of effective therapeutic strategies against viral diseases.
Dr. Mohammed Salah
Guest Editor
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Keywords
- antiviral drug discovery
- viral diseases
- computational drug design
- in silico modeling
- quantitative structure–activity relationship (QSAR)
- molecular modeling
- molecular dynamics simulations
- virtual screening
- machine learning in drug discovery
- bioactive compounds
- computer-aided drug design (CADD)
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