# Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models

arXiv: https://arxiv.org/abs/1811.07054 · HTML: https://mlsanity.com/paper/1811.07054/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [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)
- [Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare](https://mlsanity.com/paper/2105.07082/) (2021)
- [Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning](https://mlsanity.com/paper/2410.12096/) (2024)
- [DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm](https://mlsanity.com/paper/2210.00802/) (2022)
- [CongFu: Conditional Graph Fusion for Drug Synergy Prediction](https://mlsanity.com/paper/2305.14517/) (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)

## Abstract (arXiv)

Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computational models to predict and prioritize effective drug combinations is important for combinatory therapy discovery in cancer. In this study, we proposed a novel deep learning model, AuDNNsynergy, to prediction drug combinations by integrating multi-omics data and chemical structure data. In specific, three autoencoders were trained using the gene expression, copy number and genetic mutation data of all tumor samples from The Cancer Genome Atlas. Then the physicochemical properties of drugs combined with the output of the three autoencoders, characterizing the individual cancer cell-lines, were used as the input of a deep neural network that predicts the synergy value of given pair-wise drug combinations against the specific cancer cell-lines. The comparison results showed the proposed AuDNNsynergy model outperforms four state-of-art approaches, namely DeepSynergy, Gradient Boosting Machines, Random Forests, and Elastic Nets. Moreover, we conducted the interpretation analysis of the deep learning model to investigate potential vital genetic predictors and the underlying mechanism of synergistic drug combinations on specific cancer cell-lines.