mlsanityData to 7 Oct 2026

Few-Shot Graph Learning for Molecular Property Prediction

arXiv 2102.07916 ↗First posted 16 Feb 2021cs.LG cs.SIcited by 81

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Abstract (arXiv)

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network methods usually require large training dataset for each property, impairing their performances in cases (especially for new molecular properties) with a limited amount of experimental data, which are common in real situations. To this end, we propose Meta-MGNN, a novel model for few-shot molecular property prediction. Meta-MGNN applies molecular graph neural network to learn molecular representation and builds a meta-learning framework for model optimization. To exploit unlabeled molecular information and address task heterogeneity of different molecular properties, Meta-MGNN further incorporates molecular structure, attribute based self-supervised modules and self-attentive task weights into the former framework, strengthening the whole learning model. Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

27 of 49 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  8. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  9. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  10. 2016Matching Networks for One Shot Learningcited by 2,246
  11. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  12. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
15 more
  1. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  2. 2019Graph Neural Networks for Social Recommendationcited by 711
  3. 2018Meta-learning with differentiable closed-form solverscited by 395
  4. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  5. 2017A Structured Self-attentive Sentence Embeddingcited by 318
  6. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  7. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  8. 2016Low Data Drug Discovery with One-shot Learningcited by 91
  9. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  10. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  11. 2019Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspectivecited by 73
  12. 2019Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Predictioncited by 64
  13. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  14. 2019Few-Shot Knowledge Graph Completioncited by 63
  15. 2018Chemi-net: a graph convolutional network for accurate drug property predictioncited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 20; 2023: 17; 2024: 25; 2025: 11; 2026: 4.

81 papers.

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  1. 2023What can Large Language Models do in chemistry? A comprehensive benchmark on eight taskscited by 179
  2. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  3. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  4. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  5. 2022Graph-based Molecular Representation Learningcited by 51
  6. 2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
  7. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  8. 2022Linkless Link Prediction via Relational Distillationcited by 39
  9. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  10. 2022Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalizationcited by 35
  11. 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentationcited by 29
  12. 2021Hierarchical Prototype Networks for Continual Graph Representation Learningcited by 28
38 more
  1. 2024MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Predictioncited by 24
  2. 2023Context-enriched molecule representations improve few-shot drug discoverycited by 23
  3. 2022FAITH: Few-Shot Graph Classification with Hierarchical Task Graphscited by 22
  4. 2022Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Predictioncited by 21
  5. 2024Continual Learning on Graphs: Challenges, Solutions, and Opportunitiescited by 20
  6. 2022Feature Overcorrelation in Deep Graph Neural Networks: A New Perspectivecited by 19
  7. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  8. 2022ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalizationcited by 15
  9. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  10. 2022Boosting Graph Neural Networks via Adaptive Knowledge Distillationcited by 14
  11. 2023Few-shot Node Classification with Extremely Weak Supervisioncited by 14
  12. 2024Topology-aware Embedding Memory for Continual Learning on Expanding Networkscited by 14
  13. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  14. 2026Graph is a Substrate Across Data Modalitiescited by 13
  15. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  16. 2022Few-Shot Learning on Graphscited by 11
  17. 2022Link Prediction with Non-Contrastive Learningcited by 11
  18. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  19. 2024Text-guided multi-property molecular optimization with a diffusion language modelcited by 11
  20. 2023Graph Sampling-based Meta-Learning for Molecular Property Predictioncited by 10
  21. 2022Supervised Graph Contrastive Learning for Few-shot Node Classificationcited by 9
  22. 2022Robust Knowledge Adaptation for Dynamic Graph Neural Networkscited by 9
  23. 2021Meta-Learning with Graph Neural Networks: Methods and Applicationscited by 7
  24. 2024Cooperative Classification and Rationalization for Graph Generalizationcited by 7
  25. 2023A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledgecited by 6
  26. 2023Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Propertiescited by 6
  27. 2024Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Surveycited by 6
  28. 2024Instance-Aware Graph Prompt Learningcited by 6
  29. 2025Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyondcited by 6
  30. 2023CARL-G: Clustering-Accelerated Representation Learning on Graphscited by 5
  31. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 5
  32. 2024Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Datacited by 5
  33. 2024AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Modelscited by 5
  34. 2025Dual-level Mixup for Graph Few-shot Learning with Fewer Taskscited by 5
  35. 2025Nonparametric Teaching for Graph Property Learnerscited by 5
  36. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  37. 2022Metric Based Few-Shot Graph Classificationcited by 4
  38. 2023PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Predictioncited by 4

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