https://github.com/AstraZeneca/awesome-drug-pair-scoring

Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
https://github.com/AstraZeneca/awesome-drug-pair-scoring

List: awesome-drug-pair-scoring

chemistry ddi decagon deep-chemistry deep-learning drug drug-combination drug-design drug-drug-interaction drug-repurposing drug-synergy drug-target-interactions gcn gnn graph-neural-network knowledge-graph machine-learning polypharmacy relational-learning synergy-prediction

Last synced: 2 months ago
JSON representation

Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)

• Host: GitHub
• URL: https://github.com/AstraZeneca/awesome-drug-pair-scoring
• Owner: AstraZeneca
• License: apache-2.0
• Created: 2021-03-28T22:29:52.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-08-07T17:10:49.000Z (over 2 years ago)
• Last Synced: 2024-04-14T10:50:28.847Z (9 months ago)
• Topics: chemistry, ddi, decagon, deep-chemistry, deep-learning, drug, drug-combination, drug-design, drug-drug-interaction, drug-repurposing, drug-synergy, drug-target-interactions, gcn, gnn, graph-neural-network, knowledge-graph, machine-learning, polypharmacy, relational-learning, synergy-prediction
• Homepage:
• Size: 1.47 MB
• Stars: 86
• Watchers: 12
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• top-pharma50 - **AstraZeneca/awesome-drug-pair-scoring** - chemistry`, `deep-learning`, `drug`, `drug-combination`, `drug-design`, `drug-drug-interaction`, `drug-repurposing`, `drug-synergy`, `drug-target-interactions`, `gcn`, `gnn`, `graph-neural-network`, `knowledge-graph`, `machine-learning`, `polypharmacy`, `relational-learning`, `synergy-prediction`<br><img src='https://github.com/HubTou/topgh/blob/main/icons/gstars.png'> 86 <img src='https://github.com/HubTou/topgh/blob/main/icons/forks.png'> 14 <img src='https://github.com/HubTou/topgh/blob/main/icons/license.png'> Apache License 2.0 <img src='https://github.com/HubTou/topgh/blob/main/icons/last.png'> 2022-08-07 17:10:49 | (Ranked by starred repositories)
• top-pharma50 - **AstraZeneca/awesome-drug-pair-scoring** - chemistry`, `deep-learning`, `drug`, `drug-combination`, `drug-design`, `drug-drug-interaction`, `drug-repurposing`, `drug-synergy`, `drug-target-interactions`, `gcn`, `gnn`, `graph-neural-network`, `knowledge-graph`, `machine-learning`, `polypharmacy`, `relational-learning`, `synergy-prediction`<br><img src='https://github.com/HubTou/topgh/blob/main/icons/gstars.png'> 86 <img src='https://github.com/HubTou/topgh/blob/main/icons/forks.png'> 14 <img src='https://github.com/HubTou/topgh/blob/main/icons/license.png'> Apache License 2.0 <img src='https://github.com/HubTou/topgh/blob/main/icons/last.png'> 2022-08-07 17:10:49 | (Ranked by starred repositories)
• awesome-chemistry - Awesome Drug Pair Scoring - Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (Awesome Chemistry Repositories in Github)

README

        

# Awesome Drug Pair Scoring

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)

![Maturity level-0](https://img.shields.io/badge/Maturity%20Level-ML--0-red)



  



## The Survey Paper

This repository accompanies our survey paper [A Unified View of Relational Deep Learning for Drug Pair Scoring](https://arxiv.org/abs/2111.02916).

If you find the survey or this repository useful in your research, please consider citing our paper:

```bibtex

@inproceedings{pairscoring,

  title     = {A Unified View of Relational Deep Learning for Drug Pair Scoring},

  author    = {Rozemberczki, Benedek and Bonner, Stephen and Nikolov, Andriy and Ughetto, Michaël and Nilsson, Sebastian and Papa, Eliseo},

  booktitle = {Proceedings of the Thirty-First International Joint Conference on

               Artificial Intelligence, {IJCAI-22}},

  publisher = {International Joint Conferences on Artificial Intelligence Organization},

  pages     = {5564--5571},

  year      = {2022},

}

```

--------------------------------------------------------------------------------

## Contents  

1. [High Level Models](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/chapters/high_level.md)

2. [Low Level Models](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/chapters/low_level.md)

3. [Hierarchical Models](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/chapters/hierarchical.md)

4. [Datasets](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/chapters/dataset.md)  

5. [Related Survey Papers](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/chapters/survey.md)  

--------------------------------------------------------------------------------

**License**

- [Apache 2.0](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey/blob/master/LICENSE)