Computational Approaches for Protein Design
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 683
Special Issue Editors
Interests: deep learning; protein and molecular design; bayesian modelling and estimation
Interests: protein folding; docking; halogen bonding; reactive oxygen species; neurodegenerative disorders; drug-discovery; chemical education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computational approaches for protein design, as demonstrated by AI-based technologies, have shown high efficiency in protein design and accurate prediction of molecular structures. These approaches are particularly attractive when solutions for complex problems are designed using transferable learning and pre-trained models, enabling computational biologists to find explanatory models in the presence of uncertainties existing in molecular data.
Information regarding molecular sequences inherently includes uncertainties that affect the accuracy and reliability of experimental results. Therefore, computational approaches are expected to help biologists assess potential losses in in vitro design. Taking this into account, the interactions between molecules are represented within a probabilistic framework, including Bayesian and ensemble approaches.
Discovering new insights in molecular data is a key area of research within deep learning frameworks based on hierarchical feature representation. This approach has demonstrated high efficiency in learning explanatory models from underdetermined or imbalanced data.
Authors are invited to contribute research and review articles to this Special Issue to be considered for publication. The main focus of this Special Issue is on new theoretical results and reproducible applications that demonstrate the advantages of computational approaches in protein design and the prediction of molecular structures.
Topics of interest include, but are not limited to, the following:
- New insights into molecular data, particularly regarding interactions between enzymes and RNA molecules, which potentially advance the field with respect to intervention in disease such as ribosomal disorders;
- Reliable estimation of uncertainties in molecular data and explanatory models;
- The use of AI/ML techniques to predict protein structure and function;
- The use of in silico drug design and the reduction of off-target outcomes.
Dr. Livija Jakaite
Prof. Dr. Mahesh Narayan
Guest Editors
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
- computational protein design
- molecular structure prediction
- deep learning
- protein structure and function prediction
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
