Computer Simulation Insight Into Ligand-Receptor Interaction
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 April 2026 | Viewed by 4
Special Issue Editor
Interests: structure-based drug design (SBDD); medicinal chemistry; fluorescent probe development; free energy calculations; opioid receptors; protein kinases
Special Issue Information
Dear Colleagues,
Efforts to decipher the dynamic interplay between ligands and their molecular targets lies at the heart of drug discovery and design. Recent advances in molecular simulation, AI-driven modelling, and multiscale approaches have transformed our ability to explore, predict, and rationalize ligand–receptor interactions with unprecedented resolution and accuracy. This Special Issue invites cutting-edge contributions that leverage computational techniques to unravel binding mechanisms, guide hit-to-lead optimization, or enhance our understanding of receptor pharmacology.
We welcome original research, reviews, and perspectives covering state-of-the-art molecular dynamics applications, free energy calculation techniques, structure-based machine learning, and hybrid QM/MM, or physics-informed AI methods focusing on ligand–receptor interactions. Submissions addressing pharmaceutically relevant targets—such as GPCRs, enzymes, ion channels and protein–protein interfaces as well as RNA-binding small molecules—or integrating simulations with experimental and omics data are especially encouraged.
This Issue seeks to bridge disciplines and showcase innovations that push the boundaries of computational drug design and molecular recognition, accelerating translation from in silico prediction to therapeutic insight.
Dr. Antonis Drakopoulos
Guest Editor
Manuscript Submission Information
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Keywords
- structure–activity relationship (SAR)
- free energy perturbation (FEP)
- structure-based drug design (SBDD)
- machine learning in drug discovery
- hybrid qm/mm methods
- binding affinity prediction
- enhanced sampling techniques
- target binding kinetics
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