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RAS BiologyГенетика Russian Journal of Genetics

  • ISSN (Print) 0016-6758
  • ISSN (Online) 3034-5103

Genetic Technologies and Methods of Combinatorial Chemistry and Biology in the Study of Biological Processes

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PII
S303451032510051-1
DOI
10.7868/S303451032510051
Publication type
Article
Status
Published
Authors
A. G. Gabibov
  • Affiliation:
    Shemyakin-Ovchinnikov Institute of bioorganic chemistry Russian Academy of Sciences
    Lomonosov Moscow State University
V. D. Knorre
  • Affiliation: Shemyakin-Ovchinnikov Institute of bioorganic chemistry Russian Academy of Sciences
Ya. V. Solov'ev
  • Affiliation: Shemyakin-Ovchinnikov Institute of bioorganic chemistry Russian Academy of Sciences
Volume/ Edition
Volume 61 / Issue number 11
Pages
40-45
Abstract
This article provides a comprehensive review of significant advancements in the practical application of large language model (LLM) algorithms to contemporary problems in structural bioinformatics. The discussion focuses on several demonstrated successes of LLM implementations, including their use in predicting antigen surface epitopes, assessing the antigen-binding capabilities of specific CDRH3 fragments, and forecasting antibody cross-reactivity patterns. Particular attention is given to concrete examples where LLMs have been successfully employed for identifying hemagglutinin-binding antibodies against influenza virus, predicting the effects of point mutations, and improving the accuracy of protein sequence alignments. The analysis further examines critical limitations inherent in current LLM approaches, with specific emphasis on challenges related to model weight interpretability, constraints imposed by training dataset characteristics, and the substantial computational resources required for effective model training.
Keywords
машинное обучение большие языковые модели (БЯМ) предсказание кросс-реактивности предсказание эпитопов анализ эффектов мутаций проблемы применения БЯМ
Date of publication
01.11.2025
Year of publication
2025
Number of purchasers
0
Views
29

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