# DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

arXiv: https://arxiv.org/abs/2107.02467 · HTML: https://mlsanity.com/paper/2107.02467/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 86 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models](https://mlsanity.com/paper/1811.07054/) (2018)

## Built on by (most cited first, 27 of 27)

- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Molecular geometric deep learning](https://mlsanity.com/paper/2306.15065/) (2023)
- [SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design](https://mlsanity.com/paper/2307.11694/) (2023)
- [xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data](https://mlsanity.com/paper/2311.15156/) (2023)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [ChemicalX: A Deep Learning Library for Drug Pair Scoring](https://mlsanity.com/paper/2202.05240/) (2022)
- [A Unified View of Relational Deep Learning for Drug Pair Scoring](https://mlsanity.com/paper/2111.02916/) (2021)
- [Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling](https://mlsanity.com/paper/2209.09245/) (2022)
- [Predicting Drug-Drug Interactions using Deep Generative Models on Graphs](https://mlsanity.com/paper/2209.09941/) (2022)
- [Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction](https://mlsanity.com/paper/2309.06584/) (2023)
- [Recent Developments in GNNs for Drug Discovery](https://mlsanity.com/paper/2506.01302/) (2025)
- [DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm](https://mlsanity.com/paper/2210.00802/) (2022)
- [Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning](https://mlsanity.com/paper/2301.05931/) (2023)
- [Distributed representations of graphs for drug pair scoring](https://mlsanity.com/paper/2209.09383/) (2022)
- [Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs](https://mlsanity.com/paper/2302.08680/) (2023)
- [CongFu: Conditional Graph Fusion for Drug Synergy Prediction](https://mlsanity.com/paper/2305.14517/) (2023)
- [Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2306.10456/) (2023)
- [ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction](https://mlsanity.com/paper/2311.16207/) (2023)
- [MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms](https://mlsanity.com/paper/2501.07884/) (2025)
- [New methods for drug synergy prediction: a mini-review](https://mlsanity.com/paper/2404.02484/) (2024)
- [Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line-Specific Synergistic Drug Combinations](https://mlsanity.com/paper/2411.04747/) (2024)
- [A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges](https://mlsanity.com/paper/2509.07887/) (2025)
- [Disentangling Causal Substructures for Interpretable and Generalizable Drug Synergy Prediction](https://mlsanity.com/paper/2511.02146/) (2025)
- [DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery](https://mlsanity.com/paper/2604.02346/) (2026)
- [OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction](https://mlsanity.com/paper/2605.30247/) (2026)
- [ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening](https://mlsanity.com/paper/2608.12219/) (2026)
- [VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics](https://mlsanity.com/paper/2608.25841/) (2026)

## Abstract (arXiv)

Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network have recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural networks and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multi-layer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network, and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.