# Distributed representations of graphs for drug pair scoring

arXiv: https://arxiv.org/abs/2209.09383 · HTML: https://mlsanity.com/paper/2209.09383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Anonymous Walk Embeddings](https://mlsanity.com/paper/1805.11921/) (2018)
- [TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery](https://mlsanity.com/paper/2202.08320/) (2022)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [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)
- [Learning distributed representations of graphs with Geo2DR](https://mlsanity.com/paper/2003.05926/) (2020)
- [RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro](https://mlsanity.com/paper/2202.04202/) (2022)

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

- [SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design](https://mlsanity.com/paper/2307.11694/) (2023)

## Abstract (arXiv)

In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring. We argue that the real world growth and update cycles of drug pair scoring datasets subvert the limitations of transductive learning associated with distributed representations. Furthermore, we argue that the vocabulary of discrete substructure patterns induced over drug sets is not dramatically large due to the limited set of atom types and constraints on bonding patterns enforced by chemistry. Under this pretext, we explore the effectiveness of distributed representations of the molecular graphs of drugs in drug pair scoring tasks such as drug synergy, polypharmacy, and drug-drug interaction prediction. To achieve this, we present a methodology for learning and incorporating distributed representations of graphs within a unified framework for drug pair scoring. Subsequently, we augment a number of recent and state-of-the-art models to utilise our embeddings. We empirically show that the incorporation of these embeddings improves downstream performance of almost every model across different drug pair scoring tasks, even those the original model was not designed for. We publicly release all of our drug embeddings for the DrugCombDB, DrugComb, DrugbankDDI, and TwoSides datasets.