Innovative Strategies and Applications for Drug Discovery
A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Molecular Medicine".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 2743
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid advancement of computational methods has significantly transformed the landscape of drug discovery and development. Traditional processes, often time-consuming and resource-intensive, are now being complemented and, in some cases, replaced by innovative computational strategies. Techniques such as molecular docking, pharmacophore modeling, and molecular dynamics simulations are increasingly integrated with machine learning algorithms to enhance prediction accuracy and expedite the discovery of novel therapeutic agents. This Special Issue is dedicated to exploring the intersection of computational methodologies and molecular research, emphasizing how these advancements are applied to molecular modeling, prediction, and design.
We invite researchers to contribute their original research articles, reviews, and case studies that showcase the integration of computational techniques with molecular research. Contributions should focus on how these methodologies enhance our understanding of molecular interactions and accelerate the discovery of new therapeutic agents. Specific areas of interest include the following topics:
- Advanced Computational Techniques in Molecular Research:
- Development and application of novel algorithms for molecular modeling and virtual screening;
- Machine learning approaches for predicting molecular properties, protein–ligand interactions, and drug design;
- Generative models for optimizing and designing drug-like molecules.
- Integrated Computational Platforms:
- Platforms that integrate computational methods with molecular research to streamline discovery workflows;
- Tools that combine molecular data with machine learning for target identification and validation.
- Case Studies in Molecular Drug Discovery:
- Case studies demonstrating the application of computational and machine learning methods in real-world drug discovery projects;
- Examples where these methods have led to the identification of promising drug candidates or provided insights into drug resistance mechanisms.
- Benchmarking Datasets and Validation:
- Creation and utilization of datasets for training and validating computational models in molecular research;
- Comparative studies evaluating different computational and machine learning approaches in molecular contexts.
- Predictive Modeling for Molecular Properties:
- Computational methods for predicting key molecular properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET);
- Machine learning models that improve the safety profile of drug candidates.
We hope that this Special Issue will serve as a valuable platform for researchers to share their cutting-edge work and foster collaborations that push the boundaries of what is possible in drug discovery. We look forward to receiving your valuable contributions to this Special Issue.
Dr. Wanjoo Chun
Guest Editor
Manuscript Submission Information
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Keywords
- drug discovery
- computational strategies
- molecular docking
- pharmacophore modeling
- molecular dynamics simulations
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