# CongFu: Conditional Graph Fusion for Drug Synergy Prediction

arXiv: https://arxiv.org/abs/2305.14517 · HTML: https://mlsanity.com/paper/2305.14517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models](https://mlsanity.com/paper/1811.07054/) (2018)
- [A Unified View of Relational Deep Learning for Drug Pair Scoring](https://mlsanity.com/paper/2111.02916/) (2021)
- [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)

## Built on by (most cited first, 1 of 1)

- [Hypergraph Neural Network with State Space Models for Node Classification](https://mlsanity.com/paper/2508.06587/) (2025)

## Abstract (arXiv)

Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes. Limited data on drug synergy, arising from the vast number of possible drug combinations and testing costs, motivate the need for predictive methods. In this work, we introduce CongFu, a novel Conditional Graph Fusion Layer, designed to predict drug synergy. CongFu employs an attention mechanism and a bottleneck to extract local graph contexts and conditionally fuse graph data within a global context. Its modular architecture enables flexible replacement of layer modules, including readouts and graph encoders, facilitating customization for diverse applications. To evaluate the performance of CongFu, we conduct comprehensive experiments on four datasets, encompassing three distinct setups for drug synergy prediction. CongFu achieves state-of-the-art results on 11 out of 12 benchmark datasets, demonstrating its ability to capture intricate patterns of drug synergy. Through ablation studies, we validate the significance of individual layer components, affirming their contributions to overall predictive performance. Finally, we propose an explainability strategy for elucidating the effect of drugs on genes. By addressing the challenge of predicting drug synergy in untested drug pairs and utilizing our proposed explainability approach, CongFu opens new avenues for optimizing drug combinations and advancing personalized medicine.