Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design. Having witnessed the success of protein sequence pretraining, pretraining for structural data which is more informative has become a promising research topic. However, there are three major challenges facing protein structure pretraining: insufficient sample diversity, physically unrealistic modeling, and the lack of protein-specific pretext tasks. To try to address these challenges, we present the 3D Geometric Pretraining. In this paper, we propose a unified framework for protein pretraining and a 3D geometric-based, data-efficient, and protein-specific pretext task: RefineDiff (Refine the Diffused Protein Structure Decoy). After pretraining our geometric-aware model with this task on limited data(less than 1% of SOTA models), we obtained informative protein representations that can achieve comparable performance for various downstream tasks.
@article{arxiv.2302.10888,
title = {Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy},
author = {Yufei Huang and Lirong Wu and Haitao Lin and Jiangbin Zheng and Ge Wang and Stan Z. Li},
journal= {arXiv preprint arXiv:2302.10888},
year = {2023}
}