Antivirology at IgMin Research | Biology Group
Our focus is on fostering synergy among fields and promoting the swift expansion of scientific understanding.
About
Antivirology is a specialized branch of virology dedicated to understanding, preventing, and combating viral infections. This dynamic and interdisciplinary field explores the mechanisms of viral replication, transmission, and host interactions while focusing on strategies to inhibit these processes. By leveraging knowledge from molecular biology, immunology, and pharmacology, antivirology aims to uncover innovative solutions for controlling and eradicating viral diseases that impact global health.
Antivirology research delves into a wide array of topics, including antiviral drug discovery, vaccine development, and viral resistance mechanisms. It plays a pivotal role in addressing emerging viral threats, such as pandemic outbreaks and zoonotic viruses, while also contributing to advancements in therapeutic interventions for chronic viral infections. This field not only enhances our understanding of viruses but also fosters the development of technologies and approaches to mitigate their impact on society.
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Adaptation of the Physical Literacy Scale for Adults into Turkish and Examination of its Psychometric Properties
- From Traditionalism to Algorithms: Embracing Artificial Intelligence for Effective University Teaching and Learning
- Evaluating Digital Imaging Technologies for Anogenital Injury Documentation in Sexual Assault Cases
- Analytical Expressions of the Markov Chain of K-Ras4B Protein within the Catalytic Environment and a New Markov-State Model
- Assessing Bee (Hymenoptera, Apoidea, Anthophila) Diversity and Floral Preference in Two Habitats in the Iberian Peninsula
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
