# AstraZeneca — developer ecosystem report

> Data and AI: Unlocking new science insights

- GitHub org: `AstraZeneca`
- Category: ai
- Overall score: 40.0 / 100
- Overall rank: #466
- Last refreshed: 2026-08-21

## Pillar scores

| Pillar | Score / 100 | Rank | Weight in overall |
| --- | --- | --- | --- |
| Code & Polish | 57.0 | #286 | ~22% |
| Education | 75.0 | #236 | ~18% |
| AI readiness | 14.0 | #715 | part of Reach |
| Builder experience | 0.0 | #1 | ~12% |
| Community | 27.0 | #572 | ~16% |
| Reach | 44.0 | #431 | ~18% |
| Momentum | 30.0 | #499 | ~14% |

## Links

- GitHub: https://github.com/AstraZeneca
- Website: https://www.astrazeneca.com

## Public GitHub totals

| Metric | Value |
| --- | --- |
| Repos analyzed | 12 |
| Stars | 5,202 |
| Forks | 599 |
| Open issues | 224 |
| Contributors | — |

## Top repositories

| Repo | Stars | Language | Description |
| --- | --- | --- | --- |
| [awesome-explainable-graph-reasoning](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning) | 1,990 | — | A collection of research papers and software related to explainability in graph machine learning. |
| [chemicalx](https://github.com/AstraZeneca/chemicalx) | 783 | Python | A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022) |
| [rexmex](https://github.com/AstraZeneca/rexmex) | 278 | Python | A general purpose recommender metrics library for fair evaluation. |
| [awesome-drug-discovery-knowledge-graphs](https://github.com/AstraZeneca/awesome-drug-discovery-knowledge-graphs) | 266 | — | A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the p |
| [awesome-shapley-value](https://github.com/AstraZeneca/awesome-shapley-value) | 155 | — | Reading list for "The Shapley Value in Machine Learning" (JCAI 2022) |
| [biology-for-ai](https://github.com/AstraZeneca/biology-for-ai) | 149 | — | learning biology syllabus, geared for machine learning folks |
| [SubTab](https://github.com/AstraZeneca/SubTab) | 149 | Python | The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representatio |
| [jazzy](https://github.com/AstraZeneca/jazzy) | 117 | Python | Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules. |
| [onto_merger](https://github.com/AstraZeneca/onto_merger) | 102 | HTML | OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes that represent the same domain. |
| [awesome-drug-pair-scoring](https://github.com/AstraZeneca/awesome-drug-pair-scoring) | 101 | — | Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022) |
| [DiffAbXL](https://github.com/AstraZeneca/DiffAbXL) | 100 | Python | The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody S |
| [KAZU](https://github.com/AstraZeneca/KAZU) | 91 | Python | Fast, world class biomedical NER |

## Methodology

Scores are computed from public signals only (GitHub, package registries, docs sites, community platforms). Pillar definitions: https://www.smoower.com/docs.md

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