Advancing Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Through Computer-Aided Drug Design

Dear Colleagues,

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most widely used medications in modern medicine, primarily employed to treat pain, inflammation, and fever. Since the introduction of aspirin, NSAIDs have played a crucial role in clinical practice and have demonstrated significant efficacy in managing a wide range of conditions, from arthritis to acute injuries. However, as medical research has advanced, the potential side effects of NSAIDs—such as gastrointestinal damage, cardiovascular risks, and nephrotoxicity—have garnered increasing attention. These challenges have spurred scientists and the pharmaceutical industry to explore safer and more effective alternatives.

In recent years, the integration of computational tools into drug discovery has revolutionized the field, offering new avenues to address the limitations of traditional NSAIDs. Computer-aided drug design (CADD) has emerged as a powerful approach to accelerate the development of novel therapeutics, enabling researchers to predict drug–target interactions, optimize molecular structures, and identify compounds with improved efficacy and reduced side effects. By leveraging techniques such as molecular docking, molecular dynamics simulations, and machine learning, CADD provides a cost-effective and time-efficient strategy to design next-generation NSAIDs.

This Special Issue aims to explore the applicability of computer-aided drug design tools in the development of new, safer, and more effective NSAIDs. We invite contributions that highlight the latest advancements in this interdisciplinary field, including but not limited to the following:

1. Structure-based drug design: Utilizing 3D structures of target proteins (e.g., COX-1, COX-2) to design selective inhibitors with minimized off-target effects.
2. Ligand-based drug design: Employing quantitative structure–activity relationship (QSAR) models to optimize existing NSAIDs for enhanced potency and safety.
3. Virtual screening: Identifying novel NSAID candidates from large chemical libraries using computational methods.
4. Molecular dynamics simulations: Investigating the binding mechanisms and stability of NSAIDs with their targets to guide rational drug design.
5. Machine learning- and AI-driven approaches: Predicting drug properties, toxicity, and pharmacokinetics to streamline the development process.
6. Multi-target drug design: Developing NSAIDs that simultaneously modulate multiple pathways to improve therapeutic outcomes.

By bringing together cutting-edge research and innovative methodologies, this Special Issue seeks to showcase how computational tools can address the challenges associated with traditional NSAIDs. We aim to provide a platform for researchers, clinicians, and industry professionals to share insights, foster collaboration, and accelerate the development of next-generation NSAIDs that are both safer and more effective.

This Special Issue will significantly enhance bioengineering by showcasing innovative, interdisciplinary research that addresses critical challenges in drug development. It will attract high-quality submissions, foster collaboration, and position the journal as a leader in the integration of computational tools and bioengineering for medical advancements.

We invite original research articles, reviews, and perspectives that contribute to advancing the field of NSAIDs through computer-aided drug design. Together, we can pave the way for transformative breakthroughs in pain management and anti-inflammatory therapy.

Dr. Chin-Hung Lai
Dr. Suraj Narayan Mali
Guest Editors

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• molecular docking
• molecular dynamics
• QSAR
• AI
• NSAIDs