mlsanityData to 7 Oct 2026

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

arXiv 2302.10888 ↗First posted 5 Feb 2023cs.LG cs.AI q-bio.BMcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 51 references that are in the map. First posted on arXiv 5 Feb 2023.

Builds on

16 of 51 references.

References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  4. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  5. 2019Evaluating Protein Transfer Learning with TAPEcited by 229
  6. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 229
  7. 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
  8. 2022Protein structure generation via folding diffusioncited by 113
  9. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Modelscited by 97
  10. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  11. 2021Deciphering antibody affinity maturation with language models and weakly supervised learningcited by 52
  12. 2022Structure-aware Protein Self-supervised Learningcited by 33
4 more
  1. 2020Learning Optimal Representations with the Decodable Information Bottleneckcited by 32
  2. 2022Contrastive Representation Learning for 3D Protein Structurescited by 30
  3. 2022A Survey on Protein Representation Learning: Retrospect and Prospectcited by 10
  4. 2017EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representationcited by 4

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Cited by year

Citing papers per year: 2023: 3; 2024: 7.

10 papers.

  1. 2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
  2. 2024MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embeddingcited by 22
  3. 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridgecited by 16
  4. 2024Evaluating representation learning on the protein structure universecited by 16
  5. 2023A Systematic Study of Joint Representation Learning on Protein Sequences and Structurescited by 15
  6. 2023Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Predictioncited by 13
  7. 2024Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learningcited by 11
  8. 2024PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Predictioncited by 10
  9. 2024Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Designcited by 0
  10. 2024DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Predictioncited by 0