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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

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary

Keywords

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: 6 months ago · JSON representation

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

Basic Info
  • Host: GitHub
  • Owner: AstraZeneca
  • License: apache-2.0
  • Default Branch: master
  • Homepage:
  • Size: 1.47 MB
Statistics
  • Stars: 96
  • Watchers: 11
  • Forks: 14
  • Open Issues: 0
  • Releases: 0
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
Created almost 5 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.md

Awesome Drug Pair Scoring

Awesome PRs Welcome Maturity level-0

The Survey Paper

This repository accompanies our survey paper A Unified View of Relational Deep Learning for Drug Pair Scoring.

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
  2. Low Level Models
  3. Hierarchical Models
  4. Datasets
  5. Related Survey Papers

License

Owner

  • Name: AstraZeneca
  • Login: AstraZeneca
  • Kind: organization
  • Location: Global

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