# Learning Graph-Level Representation for Drug Discovery

arXiv: https://arxiv.org/abs/1709.03741 · HTML: https://mlsanity.com/paper/1709.03741/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 30 of 43)

- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [3D Infomax improves GNNs for Molecular Property Prediction](https://mlsanity.com/paper/2110.04126/) (2021)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [OOD-GNN: Out-of-Distribution Generalized Graph Neural Network](https://mlsanity.com/paper/2112.03806/) (2021)
- [Graph Data Augmentation for Graph Machine Learning: A Survey](https://mlsanity.com/paper/2202.08871/) (2022)
- [Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications](https://mlsanity.com/paper/2110.08760/) (2021)
- [Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing](https://mlsanity.com/paper/2102.08786/) (2021)
- [TxGemma: Efficient and Agentic LLMs for Therapeutics](https://mlsanity.com/paper/2504.06196/) (2025)
- [Tx-LLM: A Large Language Model for Therapeutics](https://mlsanity.com/paper/2406.06316/) (2024)
- [Context-enriched molecule representations improve few-shot drug discovery](https://mlsanity.com/paper/2305.09481/) (2023)
- [Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models](https://mlsanity.com/paper/2404.15625/) (2024)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learning](https://mlsanity.com/paper/2404.14986/) (2024)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [Characterizing the Influence of Graph Elements](https://mlsanity.com/paper/2210.07441/) (2022)
- [VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification](https://mlsanity.com/paper/2404.07194/) (2024)
- [Probabilistic Graph Rewiring via Virtual Nodes](https://mlsanity.com/paper/2405.17311/) (2024)
- [Equivariant Hypergraph Neural Networks](https://mlsanity.com/paper/2208.10428/) (2022)
- [ReactZyme: A Benchmark for Enzyme-Reaction Prediction](https://mlsanity.com/paper/2408.13659/) (2024)
- [Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task](https://mlsanity.com/paper/2110.04228/) (2021)
- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Robust Causal Bandits for Linear Models](https://mlsanity.com/paper/2310.19794/) (2023)
- [Edge but not Least: Cross-View Graph Pooling](https://mlsanity.com/paper/2109.11796/) (2021)
- [A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging](https://mlsanity.com/paper/2210.16670/) (2022)
- [Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network](https://mlsanity.com/paper/2302.02369/) (2023)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection](https://mlsanity.com/paper/2404.01578/) (2024)
- [Improved Bound for Robust Causal Bandits with Linear Models](https://mlsanity.com/paper/2405.07795/) (2024)
- [Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches](https://mlsanity.com/paper/2408.09461/) (2024)

## 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.