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Recent advances in antibody optimization based on deep learning methods
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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, 1998, -1(-1): .
@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",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2400387"
}
%0 Journal Article
%T Recent advances in antibody optimization based on deep learning methods
%A Ruofan JIN
%A Ruhong ZHOU
%A Dong ZHANG
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%I Zhejiang University Press & Springer
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T1 - Recent advances in antibody optimization based on deep learning methods
A1 - Ruofan JIN
A1 - Ruhong ZHOU
A1 - Dong ZHANG
J0 - Journal of Zhejiang University Science B
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IS - -1
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%@ 1673-1581
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B2400387
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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