# ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction

arXiv: https://arxiv.org/abs/2311.16207 · HTML: https://mlsanity.com/paper/2311.16207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://mlsanity.com/paper/2206.12811/) (2022)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation](https://mlsanity.com/paper/2302.04775/) (2023)
- [Hybrid Attentional Memory Network for Computational drug repositioning](https://mlsanity.com/paper/2006.06910/) (2020)
- [GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction](https://mlsanity.com/paper/2307.08989/) (2023)
- [The Neural Metric Factorization for Computational Drug Repositioning](https://mlsanity.com/paper/2109.07690/) (2021)
- [The Computational Drug Repositioning without Negative Sampling](https://mlsanity.com/paper/2111.14696/) (2021)
- [Self-supervised Learning for Label Sparsity in Computational Drug Repositioning](https://mlsanity.com/paper/2206.00262/) (2022)

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

- [Explainable Multimodal Regression via Information Decomposition](https://mlsanity.com/paper/2512.22102/) (2025)

## Abstract (arXiv)

Drug combination refers to the use of two or more drugs to treat a specific disease at the same time. It is currently the mainstream way to treat complex diseases. Compared with single drugs, drug combinations have better efficacy and can better inhibit toxicity and drug resistance. The computational model based on deep learning concatenates the representation of multiple drugs and the corresponding cell line feature as input, and the output is whether the drug combination can have an inhibitory effect on the cell line. However, this strategy of concatenating multiple representations has the following defects: the alignment of drug representation and cell line representation is ignored, resulting in the synergistic relationship not being reflected positionally in the embedding space. Moreover, the alignment measurement function in deep learning cannot be suitable for drug synergy prediction tasks due to differences in input types. Therefore, in this work, we propose ALNSynergy, a graph convolutional network with multi-representation alignment for predicting drug synergy. In the ALNSynergy model, we designed a multi-representation alignment function suitable for the drug synergy prediction task so that the positional relationship between drug representations and cell line representation is reflected in the embedding space. In addition, the vector modulus of drug representations and cell line representation is considered to improve the accuracy of calculation results and accelerate model convergence. Finally, many relevant experiments were run on multiple drug synergy datasets to verify the effectiveness of the above innovative elements and the excellence of the ALNSynergy model.