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 88
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
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Keywords
- proteins
- peptides
- large language models
- machine learning
- bioactive protein/peptides
- sequenced-based prediction
- deep learning
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