Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy

• URL: http://arxiv.org/abs/2302.10888v1
• Date: Sun, 5 Feb 2023 14:13:32 GMT
• Title: Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
• Authors: Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang and Stan Z. Li
• Abstract summary: Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design. In this paper, we propose a unified framework for protein pretraining and a 3D geometric-based, data-efficient, and protein-specific pretext task: RefineDiff.
• Score: 42.49977473599661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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