Matsunaga Ryo, Tsumoto Kouhei
Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
J Biomed Sci. 2025 May 9;32(1):46. doi: 10.1186/s12929-025-01141-x.
The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functional properties to train predictive models that enable rational design. This review highlights the significant advancements in data acquisition and feature extraction, emphasizing the necessity of capturing both sequence and structural information. We illustrate how machine learning models, including protein language models, are used not only to enhance affinity but also to optimize other crucial therapeutic properties, such as specificity, stability, viscosity, and manufacturability. Furthermore, we provide practical examples and case studies to demonstrate how the synergy between experimental and computational approaches accelerates antibody engineering. Finally, this review discusses the remaining challenges in fully realizing the potential of artificial intelligence (AI)-powered antibody discovery pipelines to expedite therapeutic development.
高通量实验与机器学习的整合正在改变数据驱动的抗体工程，彻底革新抗体疗法的发现与优化。这些方法利用包含抗体序列、结构和功能特性的广泛数据集来训练预测模型，从而实现理性设计。本综述重点介绍了数据采集和特征提取方面的重大进展，强调了捕获序列和结构信息的必要性。我们阐述了包括蛋白质语言模型在内的机器学习模型如何不仅用于提高亲和力，还用于优化其他关键治疗特性，如特异性、稳定性、粘度和可制造性。此外，我们提供实际示例和案例研究，以展示实验方法与计算方法之间的协同作用如何加速抗体工程。最后，本综述讨论了在充分实现人工智能驱动的抗体发现流程以加速治疗开发潜力方面仍然存在的挑战。
