Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics
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 July 2026 | Viewed by 134
Special Issue Editors
Interests: computational chemistry; CADD; ligand-based and structure-based drug design; virtual screening; QSAR modeling
Interests: cheminformatics; computational chemistry; molecular modeling; machine learning in drug design; computer-aided drug discovery
Special Issue Information
Dear Colleagues,
Computer-aided drug design (CADD) now plays an increasingly central role in pharmaceutical research, supporting the discovery and optimization of new therapeutic candidates. As computational power and data accessibility continue to grow, in silico methods allow researchers to explore chemical space more effectively, predict molecular behavior, and design biologically active compounds with greater accuracy and reduced experimental cost.
Advances in computational chemistry and cheminformatics, along with machine learning, artificial intelligence, and modern molecular modeling techniques, have greatly expanded the analytical and predictive capabilities available to researchers. These tools accelerate early-stage drug discovery, improve rational molecular design, and help prioritize molecules with favorable properties for further investigation.
This Special Issue aims to highlight recent advances, methodologies, and applications in CADD, spanning from molecular docking and molecular dynamics to data-driven cheminformatics pipelines, predictive modeling, and AI-enabled lead optimization. Contributions that integrate in silico strategies with experimental validation, predictive QSAR modeling, biomolecular target prioritization, or large-scale virtual screening are highly encouraged.
We welcome original research articles, short communications, and a limited number of review papers. Suitable topics include, but are not limited to, the following:
- Computational drug design and virtual screening;
- Cheminformatics and chemical data mining;
- Machine learning and deep learning approaches in drug discovery;
- Molecular docking, molecular dynamics, and QM/MM simulations;
- Pharmacophore modeling and QSAR/QSPR studies;
- Fragment-based and structure-based drug discovery;
- Integration of computational and experimental methodologies.
We look forward to receiving your contributions.
Dr. Ismail Hdoufane
Dr. Mehdi Oubahmane
Guest Editors
Manuscript Submission Information
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Keywords
- computer-aided drug design (CADD)
- computational chemistry
- cheminformatics
- molecular dynamics simulations
- molecular modeling
- QSAR modeling
- virtual screening
- artificial intelligence in drug discovery
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