语言模型辅助人工智能抗体设计与优化的研究热点和进展分析OACSTPCD

Objective To analyze the hotspots and developments in the field of language model-assisted artificial intelli-gence(Al)for antibody design and optimization in order to provide reference for research on development of antibodies.Methods By using CiteSpace software,hotspots of research were analyzed based on literature retrieved from the Web of Science,PubMed,and Scopus databases,focusing on three pivotal areas of research related to antibody design and optimization:the construction of pre-trained language models for antibodies,the generation of antibody sequences,and the prediction of three-dimensional structures of antibodies.In addition,this analysis reviewed the major advances in each of the specified research tasks,focusing on the delineation of similarities and differences across studies and dominating challenges in this field.Results From 2019(10 publications)to 2023(89 publications),the scale of and interest in this field kept increasing.Hotspots involved leveraging language models to assist the design or optimization of humanized,high-affinity,and highly specific antibodies.Within each research,methods were characterized by the diversity of model architectures,consistency of training data,and variations in training strategies.Challenges to the field included sparse antigen data,computational power limitations,and insufficient integration of wet and dry lab experiments.Conclusion Research in language model-assisted Al antibody design and optimization is gaining momentum and proves fruitful.However,researchers should be alert to the inadequate attention to antigen-antibody interactions and insufficient integration of experimental and computational validation,conduct more in-depth research and expand applications.