Advances in Protein–Ligand Interactions: From Structure, Function to Applications, 2nd Edition
A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Chemical Biology".
Deadline for manuscript submissions: 30 June 2026
Special Issue Editor
Interests: bioinformatics; computational geometry; computer science; data analysis; data visualization; hydrophobic core; optimization algorithms; protein folding; protein-protein interaction; python programming; web services and databases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Proteins do not exist in isolation; rather, they inhabit a crowded cellular environment replete with molecules. Similarly, numerous biological processes hinge on proteins’ interactions with their immediate surroundings. Proteins can form complexes with other proteins, small molecules, ions, nucleic acids, and nearby fragments of the cell membrane. While "ligand" is often associated with small compounds, this terminology may be extended to encompass various complex partners in different contexts.
Comprehension and prediction of protein–ligand interactions (PLIs) is fundamental to the drug discovery process. To verify if a specific compound achieves the desired biological activity, an in vitro experiment must be conducted. However, given the vast number of candidates available from diverse drug libraries, exhaustive laboratory screening is both time-consuming and prohibitively expensive. Computational methods offer a solution by reducing the search space to a manageable subset of promising candidates. The increasing volume of chemical and biological data presents new research opportunities but also underscores the need for improved approaches in PLI analysis and prediction.
In this Special Issue, we aim to compile reports on current advancements in protein–ligand interaction studies. This includes, but is not limited to, protein surface exploration, binding site recognition, protein complex prediction, molecular dynamics simulations, de novo drug design, ADME studies, biochemical assays, structural biology techniques, the application of machine learning frameworks, and results from clinical trials. Reports on theoretical models and open-source software development are also welcome, provided they are accompanied by experimental data that validate the computational results (e.g., analysis of structures from the PDB).
We look forward to receiving both research and review articles for this Special Issue.
Dr. Mateusz Banach
Guest Editor
Dr. Mateusz Banach
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- bioinformatics
- compound libraries
- computational biology
- de novo drug design
- drug discovery
- ligand binding
- machine learning
- molecular docking
- molecular dynamics
- protein complexes.
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