# Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

arXiv: https://arxiv.org/abs/1804.10850 · HTML: https://mlsanity.com/paper/1804.10850/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism](https://mlsanity.com/paper/2101.08170/) (2021)
- [Graph-based Molecular Representation Learning](https://mlsanity.com/paper/2207.04869/) (2022)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Projective Ranking-based GNN Evasion Attacks](https://mlsanity.com/paper/2202.12993/) (2022)
- [Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery](https://mlsanity.com/paper/2201.04437/) (2022)
- [A Unified View of Relational Deep Learning for Drug Pair Scoring](https://mlsanity.com/paper/2111.02916/) (2021)
- [Recent Advances in Hypergraph Neural Networks](https://mlsanity.com/paper/2503.07959/) (2025)
- [Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2105.13975/) (2021)
- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2208.11267/) (2022)
- [GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery](https://mlsanity.com/paper/2304.05498/) (2023)
- [DDI Prediction via Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2207.05672/) (2022)
- [Neural Embeddings for Protein Graphs](https://mlsanity.com/paper/2306.04667/) (2023)
- [Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k](https://mlsanity.com/paper/2401.04741/) (2024)
- [Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks](https://mlsanity.com/paper/2504.16360/) (2025)
- [CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2607.02928/) (2026)

## Abstract (arXiv)

Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.