Allosteric Drug Design in the AI Era
A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "AI in Drug Development".
Deadline for manuscript submissions: 15 May 2026 | Viewed by 26
Special Issue Editors
2. Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood WD6 4PJ, UK
Interests: pocket mapping; cheminformatics; drug repurposing; drug discovery; protein degradation; allosteric pockets
Interests: G-protein coupled receptors; structure-based drug design; molecular dynamics; allosteric modulation; peptide drugs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Allosteric modulation has emerged as a pivotal paradigm in modern pharmacology, offering unique advantages over traditional orthosteric approaches. By targeting topographically distinct sites, allosteric ligands can modulate protein function with greater selectivity and reduced toxicity, thereby expanding the therapeutic landscape across diverse disease areas, including oncology, neurodegeneration, and immune disorders. Nevertheless, the rational discovery and optimization of allosteric modulators remain inherently challenging due to the elusive and dynamic nature of allosteric sites, the conformational heterogeneity of protein structures, and the limited availability of experimental data that comprehensively describe allosteric mechanisms.
Recent advances in artificial intelligence (AI) and machine learning are transforming this landscape. AI-driven algorithms, including deep learning, generative models, and integrative molecular simulations, now enable the systematic identification of cryptic allosteric pockets, the prediction of conformational ensembles, and the in silico design of ligand scaffolds with tailored modulatory profiles. The integration of these computational approaches with high-resolution structural techniques, molecular dynamics, and experimental pharmacology holds the potential to accelerate the transition from theoretical models of allostery to clinically translatable therapeutics.
This Special Issue aims to assemble contributions that exemplify the convergence of allosteric drug design and AI methodologies. We invite original research articles, reviews, and perspectives that (i) advance methodological frameworks for the prediction and characterization of allosteric sites, (ii) present case studies demonstrating the application of AI in allosteric ligand discovery and optimization, or (iii) provide conceptual and mechanistic insights into the role of allostery in disease biology.
By consolidating multidisciplinary efforts, this collection seeks to provide a comprehensive overview of current achievements while delineating future directions at the intersection of allostery, computational intelligence, and therapeutic innovation.
Dr. Lydia Siragusa
Dr. Antonella Ciancetta
Guest Editors
Manuscript Submission Information
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Keywords
- allosteric modulation
- artificial intelligence in drug discovery
- computational pharmacology
- allosteric sites prediction
- structure-based drug design
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