Applications of Large Language Models to Identify and Predict Bio-Activity of Proteins and Peptides
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 20 September 2025 | Viewed by 104
Special Issue Editor
Interests: bioelectronics; biological information processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
A natural protein or peptide can be thought of as a sentence consisting of a linear chain of residues and a vocabulary of 20 standard amino acids. The order in which amino acids are arranged determines the tertiary structure of proteins in their environment, which in turn gives them specific functions, which we can understand as the meaning of protein/peptide sentences. Understanding the relationship between protein sequences, structure, and function has long been a major focus of biological research. With the development of deep learning, especially pre-trained language models, the development of machine learning models based only on sequences but that can capture the structural and functional properties of proteins has been a field that researchers around the world have been vigorously expanding. Based on the above ideas, many protein language models (PLMs) have been developed, such as UniRep, ESM1, ESM2, and ESM3. Through sequence "semantic" pre-training, PLMs learn the fundamentals of protein structure and function, enabling them to perform a wide range of protein modeling and design tasks. Further expanding the development and application of such PLM models is the focus of this Special Issue. We welcome submissions of research on, but not limited to, the following topics:
- Development and fine-tuning of protein/peptide large language models;
- Sequence function recognition based on protein/peptide large language model embedding features;
- Molecular activity prediction and validation combined with protein/peptide large language models;
- Machine learning combined with a protein/peptide large language model to identify the data imbalance of molecular function;
- Research on the relationship between protein, peptide, DNA, and RNA sequences combined with language models;
- Interaction studies of proteins/peptides combined with language models and drugs, RNA, and proteins/peptides;
- Virtual screening of various drugs for proteins/peptides combined with language models.
Dr. Zhibin Lv
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
- proteins
- peptides
- large language models
- machine learning
- bioactive protein/peptides
- sequenced-based prediction
- deep learning
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.
