New Insights in Artificial Intelligence for Drug Design and Target Discovery
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 312
Special Issue Editor
Interests: artificial intelligence in drug design; computational biology; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI)-based technologies are having a transformative impact on drug design research. With the advancement of biomedical research, the integration of AI with traditional methodologies is unlocking innovative approaches to address complex challenges in drug development.
In recent years, emerging multimodal large language models, including text-based and biological data-driven foundational models, have become powerful tools for analyzing biomedical text and extracting valuable information on proteins, nucleic acids, and chemical compounds. These models facilitate the identification of novel drug candidates and accelerate biomolecule target discovery. Deep generative models represent another frontier in drug design, enabling the creation of novel chemical entities with desired properties. Furthermore, integrating multi-omics data with AI methods is crucial for understanding drug responses, identifying biomarkers, and discovering potential drug combinations. AI-based network analysis methods are also bridging traditional Chinese medicine (TCM) with modern approaches, unveiling complex disease treatment mechanisms.
We invite researchers to contribute original research articles and review papers that explore the promising applications of AI in the fields of biology and medicine. This Special Issue aims at providing novel insights into the evolving research paradigms of drug design and target discovery by integrating diverse AI methodologies, computational biology techniques, and bioinformatics.
Topics of interest include, but are not limited to, the following:
- Applications of large pre-trained language models in drug design;
- Drug design using deep generative models;
- New drug prediction frameworks based on deep learning;
- Prediction of biomolecule binding sites;
- Application of multi-omics analysis and AI methods in drug design;
- Integration of QSAR modeling, virtual screening, molecular dynamics (MD) simulations, and AI models (e.g., AlphaFold or ESMFold) in drug discovery;
- Design and prediction of bioactive peptides;
- Applications of AI-based network analysis in traditional Chinese medicine (TCM) research.
Dr. Yanjing Wang
Guest Editor
Manuscript Submission Information
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Keywords
- pre-trained language models
- generative models
- drug prediction
- binding site prediction
- peptides
- multi-omics
- deep learning
- QSAR modeling
- molecular dynamics (MD) simulations
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