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- W4384283595 endingPage "71" @default.
- W4384283595 startingPage "57" @default.
- W4384283595 abstract "Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development." @default.
- W4384283595 created "2023-07-15" @default.
- W4384283595 creator A5031263213 @default.
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- W4384283595 creator A5082960075 @default.
- W4384283595 creator A5089061668 @default.
- W4384283595 creator A5090273926 @default.
- W4384283595 date "2023-10-01" @default.
- W4384283595 modified "2023-10-01" @default.
- W4384283595 title "Deep learning in preclinical antibody drug discovery and development" @default.
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