He Xin-Heng, Li Jun-Rui, Xu James, Shan Hong, Shen Shi-Yi, Gao Si-Han, Xu H Eric
State Key Laboratory of Drug Research and CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Acta Pharmacol Sin. 2025 Mar;46(3):565-574. doi: 10.1038/s41401-024-01380-y. Epub 2024 Sep 30.
Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential, optimizing antibodies for superior efficacy presents significant challenges in both monetary and time costs. Recent strides in computational and artificial intelligence (AI), especially generative diffusion models, have begun to address these challenges, offering novel approaches for antibody design. This review delves into specific diffusion-based generative methodologies tailored for antibody design tasks, de novo antibody design, and optimization of complementarity-determining region (CDR) loops, along with their evaluation metrics. We aim to provide an exhaustive overview of this burgeoning field, making it an essential resource for leveraging diffusion-based generative models in antibody design endeavors.
治疗性抗体处于生物治疗的前沿，因其高靶标特异性和结合亲和力而受到重视。尽管它们具有潜力，但要优化抗体以实现卓越疗效，在金钱和时间成本方面都面临重大挑战。计算和人工智能（AI）领域的最新进展，尤其是生成扩散模型，已开始应对这些挑战，为抗体设计提供了新方法。本文综述深入探讨了针对抗体设计任务、从头抗体设计和互补决定区（CDR）环优化的基于扩散的特定生成方法，以及它们的评估指标。我们旨在全面概述这一新兴领域，使其成为在抗体设计工作中利用基于扩散的生成模型的重要资源。
