Recent advances in antibody optimization based on deep learning methods.

Journal: Journal of Zhejiang University. Science. B
Published Date:

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.

Authors

  • Ruofan Jin
    College of Life Science, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Ruhong Zhou
    ZheJiang University, 688 Yuhangtang Road, Hangzhou, 310027, China.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.