GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation

Abstract

Increasing works for antibody design are emerging to generate sequences and structures in Complementarity Determining Regions (CDRs), but problems still exist. We focus on two of them: (i) authenticity of the generated structure and (ii) rationality of the affinity maturation, and propose GeoAB as a solution. In specific, GeoAB-Designergenerates CDR structures with realistic internal geometries, composed of a generative geometry initializer (Geo-Initializer) and a position refiner (Geo-Refiner); GeoAB-Optimizer achieves affinity maturation by accurately predicting both the mutation effects and structures of mutant antibodies with the same network architecture as Geo-Refiner. Experiments show that GeoAB achieves state-of-the-art performance in CDR co-design and mutation effect predictions, and fulfills the discussed tasks effectively.

Cite

Text

Lin et al. "GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation." International Conference on Machine Learning, 2024.

Markdown

[Lin et al. "GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/lin2024icml-geoab/)

BibTeX

@inproceedings{lin2024icml-geoab,
  title     = {{GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation}},
  author    = {Lin, Haitao and Wu, Lirong and Huang, Yufei and Liu, Yunfan and Zhang, Odin and Zhou, Yuanqing and Sun, Rui and Li, Stan Z.},
  booktitle = {International Conference on Machine Learning},
  year      = {2024},
  pages     = {30346-30361},
  volume    = {235},
  url       = {https://mlanthology.org/icml/2024/lin2024icml-geoab/}
}