EDITORIAL article
Front. Drug Discov.
Sec. In silico Methods and Artificial Intelligence for Drug Discovery
Volume 5 - 2025 | doi: 10.3389/fddsv.2025.1632015
This article is part of the Research TopicEnhancing Drug Discovery Through Structure-Based Design and Computational TechniquesView all 5 articles
Editorial: Enhancing drug discovery through structure-based design and computational techniques
Provisionally accepted
- 1Chung-Ang University, Seoul, Republic of Korea
- 2Pere Virgili Health Research Institute (IISPV), Tarragona, Catalonia, Spain
- 3Department of Biotechnology, Faculty of Science, Siddharth University Kapilvastu, Uttar Pradesh, India
- 4Victoria University of Wellington, Wellington, Wellington, New Zealand
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algorithms and programs are widely used by scientists in both academia and industry to streamline and enhance the drug development process. As a result, several drugs developed with the help of computational tools have successfully reached the market and are continuously being used for the prevention and treatment of various diseases (Pathak et al., 2020).As a hallmark of 21 st-century science, bioinformatics, through computer-based investigations, has significantly revolutionized biological and pharmaceutical research.Recognizing its importance in drug discovery, numerous databases and tools have been developed, and many more are continuously being introduced to support the drug discovery process. Computational tools have made a significant contribution in resolving the drug target structure and drug-target interactions related issues very precisely. Additionally, existing resources are regularly updated to enhance their utility and facilitate ongoing drug discovery projects (Zhang et al., 2025). These computational tools enable the screening of large chemical libraries to identify potential lead compounds, thereby saving both time and resources. Among the core strategies in computer-aided drug discovery is structure-based design, which includes structural modeling, binding site prediction, molecular docking, virtual screening, ADMET prediction, molecular dynamics simulations, and binding energy calculations using the MM-PB/GBSA approach (Batool et al., 2019;Genheden and Ryde, 2015;Sadybekov and Katritch, 2023).
Keywords: protein modeling, Binding site prediction, molecular docking, Virtual Screening, ADMET prediction, Molecular Dynamics Simulation, Binding energy calculation
Received: 20 May 2025; Accepted: 26 May 2025.
Copyright: © 2025 Pathak, Singh and Nguyen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Rajesh Kumar Pathak, Chung-Ang University, Seoul, Republic of Korea
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.