# Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

arXiv: https://arxiv.org/abs/2310.11466 · HTML: https://mlsanity.com/paper/2310.11466/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [Towards Unsupervised Deep Graph Structure Learning](https://mlsanity.com/paper/2201.06367/) (2022)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2211.11214/) (2022)
- [Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration](https://mlsanity.com/paper/2306.13769/) (2023)
- [Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs](https://mlsanity.com/paper/2306.05628/) (2023)
- [Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework](https://mlsanity.com/paper/2305.10758/) (2023)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy](https://mlsanity.com/paper/2302.10888/) (2023)
- [Non-equispaced Fourier Neural Solvers for PDEs](https://mlsanity.com/paper/2212.04689/) (2022)
- [Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation](https://mlsanity.com/paper/2210.02099/) (2022)
- [CCPL: Cross-modal Contrastive Protein Learning](https://mlsanity.com/paper/2303.11783/) (2023)
- [Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning](https://mlsanity.com/paper/2310.02964/) (2023)

## Built on by (most cited first, 13 of 13)

- [PPFlow: Target-aware Peptide Design with Torsional Flow Matching](https://mlsanity.com/paper/2405.06642/) (2024)
- [MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding](https://mlsanity.com/paper/2402.14391/) (2024)
- [Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge](https://mlsanity.com/paper/2402.11459/) (2024)
- [Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning](https://mlsanity.com/paper/2405.10348/) (2024)
- [PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction](https://mlsanity.com/paper/2402.08198/) (2024)
- [GLProtein: Global-and-Local Structure Aware Protein Representation Learning](https://mlsanity.com/paper/2506.06294/) (2025)
- [Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling](https://mlsanity.com/paper/2601.06214/) (2026)
- [GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning](https://mlsanity.com/paper/2408.00057/) (2024)
- [MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign](https://mlsanity.com/paper/2408.10247/) (2024)
- [Exploring zero-shot structure-based protein fitness prediction](https://mlsanity.com/paper/2504.16886/) (2025)
- [Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design](https://mlsanity.com/paper/2411.17795/) (2024)
- [DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction](https://mlsanity.com/paper/2411.17798/) (2024)
- [Demystify Protein Generation with Hierarchical Conditional Diffusion Models](https://mlsanity.com/paper/2507.18603/) (2025)

## Abstract (arXiv)

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.