JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE B

Accepted manuscript available online (unedited version)

Recent advances in antibody optimization based on deep learning methods

Author(s): Ruofan JIN, Ruhong ZHOU, Dong ZHANG
Affiliation(s): Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; more
Corresponding email(s): rhzhou@zju.edu.cn, zhangd_iqb@zju.edu.cn
Key Words: Deep learning; Antibody optimization; Available datasets; Input data types

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Ruofan JIN, Ruhong ZHOU, Dong ZHANG. Recent advances in antibody optimization based on deep learning methods[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2400387

@article{title="Recent advances in antibody optimization based on deep learning methods",
author="Ruofan JIN, Ruhong ZHOU, Dong ZHANG",
journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2400387"
}

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A1 - Ruofan JIN
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doi="https://doi.org/10.1631/jzus.B2400387"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Antibodies currently comprise the predominant treatment modality for a variety of diseases, thus 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 give a brief review of 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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