mlsanityData to 7 Oct 2026

Learning Graph-Level Representation for Drug Discovery

arXiv 1709.03741 ↗First posted 12 Sep 2017cs.LG stat.MLcited by 43

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Abstract (arXiv)

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 10; 2023: 7; 2024: 13; 2025: 2; 2026: 1.

43 papers.

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  1. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  2. 20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
  3. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  4. 2021Geometric Deep Learning on Molecular Representationscited by 113
  5. 2021OOD-GNN: Out-of-Distribution Generalized Graph Neural Networkcited by 78
  6. 2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
  7. 2021Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implicationscited by 50
  8. 2021Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passingcited by 46
  9. 2025TxGemma: Efficient and Agentic LLMs for Therapeuticscited by 40
  10. 2023Context-enriched molecule representations improve few-shot drug discoverycited by 23
  11. 2024Tx-LLM: A Large Language Model for Therapeuticscited by 23
  12. 2022Graph Neural Networks for Moleculescited by 21
31 more
  1. 2024Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Modelscited by 21
  2. 2023KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategycited by 19
  3. 2024textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learningcited by 19
  4. 2022Characterizing the Influence of Graph Elementscited by 16
  5. 2024Probabilistic Graph Rewiring via Virtual Nodescited by 15
  6. 2022Equivariant Hypergraph Neural Networkscited by 14
  7. 2024VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identificationcited by 14
  8. 2024ReactZyme: A Benchmark for Enzyme-Reaction Predictioncited by 13
  9. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  10. 2023Robust Causal Bandits for Linear Modelscited by 4
  11. 2021Edge but not Least: Cross-View Graph Poolingcited by 3
  12. 2021Hybrid Graph Embedding Techniques in Estimated Time of Arrival Taskcited by 3
  13. 2022A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimagingcited by 3
  14. 2023Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Networkcited by 3
  15. 2022Self-supervised Graph-based Point-of-interest Recommendationcited by 2
  16. 2024Improved Bound for Robust Causal Bandits with Linear Modelscited by 2
  17. 2024Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approachescited by 2
  18. 2024Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Predictioncited by 2
  19. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  20. 2024GLEMOS: Benchmark for Instantaneous Graph Learning Model Selectioncited by 1
  21. 2025A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Designcited by 1
  22. 2021GABO: Graph Augmentations with Bi-level Optimizationcited by 0
  23. 2022Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Networkcited by 0
  24. 2022Tired of Over-smoothing? Stress Graph Drawing Is All You Need!cited by 0
  25. 2023Promoting Fairness in GNNs: A Characterization of Stabilitycited by 0
  26. 2023Current Methods for Drug Property Prediction in the Real Worldcited by 0
  27. 2023Heterogenous Memory Augmented Neural Networkscited by 0
  28. 2024Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Datacited by 0
  29. 2024Graph Residual based Method for Molecular Property Predictioncited by 0
  30. 2024Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Predictioncited by 0
  31. 2026GeoGAE: Scalable Graph-Level Autoencoding via Hyperball Cloud Representationscited by 0