Jin Ruofan, Zhou Ruhong, Zhang Dong
Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China.
J Zhejiang Univ Sci B. 2025 May 28;26(5):409-420. doi: 10.1631/jzus.B2400387.
Antibodies currently comprise the predominant treatment modality for a variety of diseases; therefore, optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development. Inspired by the great success of artificial intelligence-based algorithms, especially deep learning-based methods in the field of biology, various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization. Herein, we briefly review recent progress in deep learning-based antibody optimization, focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models. Furthermore, we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.
目前，抗体是多种疾病的主要治疗方式；因此，快速有效地优化其特性是基于抗体的药物开发中不可或缺的一步。受基于人工智能的算法，尤其是生物学领域基于深度学习的方法取得的巨大成功的启发，各种计算方法已被引入抗体优化中，以降低成本并提高先导候选物生成和优化的成功率。在此，我们简要回顾基于深度学习的抗体优化的最新进展，重点关注对于构建合适的深度学习模型至关重要的可用数据集和算法输入数据类型。此外，我们讨论了通用深度学习算法在抗体优化未来发展中的当前挑战和潜在解决方案。
