用大模型精准预测抗体对新冠刺突蛋白的结合亲和力
AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
- 基于语言模型学习抗体序列与抗原结合规律
- 可高精度预测抗体对新冠刺突蛋白的结合亲和力
- 适合抗体药物研发人员快速筛选候选抗体
基于机器学习的抗体设计正成为应对传染病最有力的方法之一，得益于人工智能的进展和新冠相关实验抗体数据的爆炸式增长。抗体与抗原的结合能力（即结合亲和力）是设计中和抗体最关键的性质之一。本研究提出 AbAffinity，一种新型大语言模型，能够准确预测抗体对目标肽段（如 SARS-CoV-2 刺突蛋白）的结合亲和力。代码与模型已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。