Artificial Intelligence Advancing Computer-Aided Drug Discovery
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: 30 January 2026 | Viewed by 22
Special Issue Editor
Interests: computational biology; molecular modeling; drug design; protein structure and dynamics; protein conformational disorders
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) is transforming drug development by tackling complex challenges in biomedical research and significantly expediting the discovery process. Advanced machine learning and deep learning techniques improve bioinformatics and big data analysis, facilitating the precise identification of therapeutic targets, effective high-throughput virtual screening, and the reliable prediction of binding affinities, all essential for rational drug design. AI-driven models now incorporate information from protein dynamics and biomolecular simulations, accurately reflecting the structural flexibility and conformational alterations of biological targets which are crucial for developing highly selective and successful drug development.
To further streamline candidate selection and reduce downstream failures, AI is essential in the early-stage evaluation of pharmacokinetic and toxicological profiles, such as absorption, distribution, metabolism, excretion, and toxicity. Clinical development timeframes, research expenses, and the possibility of success are drastically reduced by using AI in large biological datasets and advanced predictive algorithms. Ultimately, this revolutionary technology does more than speed up the administration of safer, more focused, and more effective drugs; it also improves patient outcomes and quality of life.
This Special Issue, titled “Artificial Intelligence Advancing Computer-Aided Drug Discovery”, highlights the recent progress of applying AI in computer-aided drug discovery. Submissions of original research and reviews on AI-assisted approaches, including target identification, compound screening, ADMET prediction, and multi-omics integration, are welcome. Studies on innovative algorithms, hybrid experimental–computational workflows, and AI-driven drug development are also encouraged.
Dr. Raju Dash
Guest Editor
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Keywords
- machine learning
- deep learning
- computer-aided drug design
- target identification
- AI-assisted molecular diagnosis
- AI-driven pharmacokinetic profiling
- virtual screening
- network pharmacology
- drug repurposing
- molecular dynamics simulation
- computational chemistry
- AI in nanotechnology
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