Artificial Intelligence in Drug Design: Molecular Aspects
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: 31 August 2026 | Viewed by 185
Editor
Interests: regenerative medicine; organ bioengineering; transplantation; drug discovery; immunosuppression
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in artificial intelligence and machine learning have significantly accelerated the process of drug discovery. By using large datasets encompassing protein structures, compound activities, and selectivity profiles, AI-driven techniques can discern complex patterns and relationships. The use of artificial intelligence for data analysis has advanced in-silico methods in pre-clinical research, saving time and cost compared to conventional methods. One of the recent examples of in-silico functional assays is GPCRVS, an AI-driven decision support system developed specifically to facilitate the web-based in-silico screening of GPCR drug candidates. Since the GPCRVS's first release in 2023, many AI-driven tools have been developed to enable massive data processing for drug design purposes. The main strength of such in-silico methods, compared to experimental approaches, is screening against many possible drug targets to minimize the risk of off-target effects, to enhance drug safety, and to increase the probability of clinical success.
The current issue is aimed at computational methods for drug discovery driven by artificial intelligence that enable the benefit from Big Data collected so far in various successful or even failed pharmaceutical campaigns. Detailed reviews comparing the usage of AI tools to discover new drugs are welcome. Research articles presenting new approaches to in-silico drug design are in the scope of this issue. Studies showing efficient integration of in-silico methods with experimental validation will receive a high acceptance rate. Applications of artificial intelligence and machine learning to medical imaging, offering unprecedented accuracy in disease detection and diagnostic support, are welcome. The current issue is not limited to G protein-coupled receptors, and articles presenting the usage of AI in drug discovery regarding other disease-associated signaling pathways, e.g., kinase signaling, will be accepted. Both intra- and intercellular signaling are within the scope of this issue, with emphasis on metastasis prevention and cell apoptosis induced in anticancer therapies.
Dr. Dorota Latek
Guest Editor
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Keywords
- artificial intelligence
- machine learning
- drug design
- medical imaging
- apoptosis
- metastasis
- anticancer therapy
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