AbAffinity：一种用于预测抗SARS-CoV-2病毒抗体结合亲和力的大语言模型
AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
AI总结：
AbAffinity是一种通过大语言模型预测抗SARS-CoV-2病毒抗体结合亲和力的新型方法。
摘要：
基于机器学习的抗体设计正成为对抗传染病的一种最有前途的方法，这归功于人工智能领域的重要进展以及实验抗体数据（特别是与新冠相关数据）的指数级增长。抗体与抗原结合的能力（称为结合亲和力）是设计中和抗体时最关键的属性之一。在本研究中，我们介绍了Ab-Affinity，一种新的大语言模型，能够准确预测抗体对目标肽（例如SARS-CoV-2刺突蛋白）的结合亲和力。代码和模型可在https://github.com/ucrbioinfo/AbAffinity上获得。
英文摘要：
Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS-CoV-2 spike protein. Code and model are available at https://github.com/ucrbioinfo/AbAffinity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
