mlsanityData to 13 Oct 2026

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction

arXiv 2210.03745 ↗First posted 7 Oct 2022q-bio.QM cs.AI cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ProGReST, the rationale is obtained along with prediction due to the model's built-in interpretability. Additionally, we introduce a new graph prototype projection to accelerate model training. Finally, we evaluate PRoGReST on a wide range of chemical datasets for molecular property prediction and perform in-depth analysis with chemical experts to evaluate obtained interpretations. Our method achieves competitive results against state-of-the-art methods.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

26 of 47 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,055
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,471
  6. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,316
  7. 2018Sanity Checks for Saliency Mapscited by 1,152
  8. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  9. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 736
  10. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 735
  11. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 648
  12. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
14 more
  1. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  2. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 260
  3. 2017Distilling a Neural Network Into a Soft Decision Treecited by 235
  4. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 214
  5. 2019Explainability Techniques for Graph Convolutional Networkscited by 161
  6. 2020Drug discovery with explainable artificial intelligencecited by 118
  7. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  8. 2021ProtGNN: Towards Self-Explaining Graph Neural Networkscited by 96
  9. 2020Molecule Attention Transformercited by 92
  10. 2020ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discoverycited by 89
  11. 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
  12. 2021Relative Molecule Self-Attention Transformercited by 21
  13. 2022Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinitycited by 7
  14. 2020Comparison of Atom Representations in Graph Neural Networks for Molecular Property Predictioncited by 5

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 2.

8 papers.

  1. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  2. 2023ICICLE: Interpretable Class Incremental Continual Learningcited by 24
  3. 2024LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Visioncited by 15
  4. 2025B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Datacited by 4
  5. 2024FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretabilitycited by 3
  6. 2025SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligencecited by 3
  7. 2026ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classificationcited by 0
  8. 2026WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testingcited by 0