基于深度学习方法的抗体优化研究进展

金若凡; 周如鸿; 张冬

doi:10.1631/jzus.B2400387

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Review | Updated：2025-05-28

基于深度学习方法的抗体优化研究进展
Recent advances in antibody optimization based on deep learning methods
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2025年26卷第5期页码：409-420
Affiliations：

1.Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
2.Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
Funds：

the National Natural Science Foundation of China(12104396);the National Key R&D Program of China(2021YFF1200404;2021YFA1201200);the National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects(ZJZX2020014);the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(SN-ZJU-SIAS-003);the Shanghai Artificial Intelligence Lab(P22KN00272)
DOI：10.1631/jzus.B2400387
中图分类号：
CSTR：
收稿日期：2024-07-24，

修回日期：2024-11-09，

纸质出版日期：2025-05-28
Accepted：

金若凡,周如鸿,张冬.基于深度学习方法的抗体优化研究进展[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(05):409-420.

Ruofan JIN, Ruhong ZHOU, Dong ZHANG. Recent advances in antibody optimization based on deep learning methods[J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(5): 409-420.
金若凡,周如鸿,张冬.基于深度学习方法的抗体优化研究进展[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(05):409-420. DOI： 10.1631/jzus.B2400387.

Ruofan JIN, Ruhong ZHOU, Dong ZHANG. Recent advances in antibody optimization based on deep learning methods[J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(5): 409-420. DOI： 10.1631/jzus.B2400387.

摘要

当前，抗体已成为多种疾病治疗的重要工具，快速高效地优化其理化性质是抗体药物开发过程中的关键环节。随着人工智能技术发展，特别是深度学习方法在生物学领域中取得的显著突破，各类计算方法被广泛应用于抗体优化环节中，以降低开发成本并提升抗体优化的成功率。本文对近年来基于深度学习的抗体优化策略进行综述，重点探讨了深度学习模型构建中关键的数据集资源及算法输入的数据类型；分析了当前深度学习算法在抗体优化应用中所面临的主要挑战；展望了未来通用型深度学习算法在该领域的发展方向及潜在的解决策略。

Abstract

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.

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references

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