# A Unified View of Relational Deep Learning for Drug Pair Scoring

arXiv: https://arxiv.org/abs/2111.02916 · HTML: https://mlsanity.com/paper/2111.02916/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 99 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 27 of 27)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [Accurate Learning of Graph Representations with Graph Multiset Pooling](https://mlsanity.com/paper/2102.11533/) (2021)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks](https://mlsanity.com/paper/2004.14949/) (2020)
- [Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2010.11711/) (2020)
- [SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization](https://mlsanity.com/paper/2010.01450/) (2020)
- [GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions](https://mlsanity.com/paper/2005.05537/) (2020)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions](https://mlsanity.com/paper/1905.09558/) (2019)
- [CASTER: Predicting Drug Interactions with Chemical Substructure Representation](https://mlsanity.com/paper/1911.06446/) (2019)
- [Drug-Drug Adverse Effect Prediction with Graph Co-Attention](https://mlsanity.com/paper/1905.00534/) (2019)
- [Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders](https://mlsanity.com/paper/1804.10850/) (2018)
- [Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models](https://mlsanity.com/paper/1811.07054/) (2018)
- [Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2006.14002/) (2020)
- [Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2105.13975/) (2021)
- [MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy](https://mlsanity.com/paper/2110.15087/) (2021)
- [Drug-drug interaction prediction based on co-medication patterns and graph matching](https://mlsanity.com/paper/1902.08675/) (2019)
- [DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction](https://mlsanity.com/paper/1704.08432/) (2017)
- [Knowledge Graph Completion to Predict Polypharmacy Side Effects](https://mlsanity.com/paper/1810.09227/) (2018)
- [Tri-graph Information Propagation for Polypharmacy Side Effect Prediction](https://mlsanity.com/paper/2001.10516/) (2020)

## Built on by (most cited first, 7 of 7)

- [Conditional Graph Information Bottleneck for Molecular Relational Learning](https://mlsanity.com/paper/2305.01520/) (2023)
- [Large Language Models are Good Relational Learners](https://mlsanity.com/paper/2506.05725/) (2025)
- [MolTC: Towards Molecular Relational Modeling In Language Models](https://mlsanity.com/paper/2402.03781/) (2024)
- [Shift-Robust Molecular Relational Learning with Causal Substructure](https://mlsanity.com/paper/2305.18451/) (2023)
- [ChemicalX: A Deep Learning Library for Drug Pair Scoring](https://mlsanity.com/paper/2202.05240/) (2022)
- [Distributed representations of graphs for drug pair scoring](https://mlsanity.com/paper/2209.09383/) (2022)
- [CongFu: Conditional Graph Fusion for Drug Synergy Prediction](https://mlsanity.com/paper/2305.14517/) (2023)

## Abstract (arXiv)

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets and evaluation protocols. In addition, we emphasize possible high impact applications and important future research directions in this domain.