  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.
