# volkamerlab/teachopencadd

TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data

Repository: https://github.com/volkamerlab/teachopencadd
Canonical: https://ross.abutalabs.com/products/teachopencadd
Homepage: https://projects.volkamerlab.org/teachopencadd
Language: Jupyter Notebook
License: CC-BY-4.0
License Family: other
Last push: 2026-08-26T14:03:50+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 81, longevity 100
- inputs: {"age_days": 2854, "days_push": 7, "days_rel": 127, "gap_med": 6, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1054, forks 242 (observed 2026-08-28T04:03:23.928297+00:00)

## What it is
TeachOpenCADD is a teaching platform for computer-aided drug design (CADD) built from interactive Jupyter notebooks ('talktorials') that use open source packages like rdkit, pypdb, biopandas, nglview, and mdanalysis. It covers both theory and practice of central CADD topics, including recent deep learning notebooks, and is installable via pip.

## Use cases
- learn computer-aided drug design with python
- teach a cheminformatics course with jupyter notebooks
- find example pipelines for ligand-based and structure-based drug design
- get started with rdkit and pypdb for structural bioinformatics
- learn deep learning applications in drug discovery
- use reproducible CADD notebooks as a starting point for research projects

## When to choose
- you are a student or researcher new to CADD wanting guided, hands-on tutorials
- you need teaching material combining biological/chemical and computational perspectives
- you want open source, citable notebook pipelines to adapt for your own projects

## When to avoid
- you need production-ready drug discovery software rather than educational material
- you require a polished GUI application for molecular modeling
- you need comprehensive documentation of a single tool instead of topic-based tutorials

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, developer-tools
- domain: education, bioinformatics, chemistry, artificial-intelligence, tutorials
- platform: python, cross-platform, cli
- tags: cheminformatics, structural-bioinformatics, drug-design, jupyter-notebooks, rdkit, molecular-dynamics, teaching-material, open-source-science, education

## Member repositories
- volkamerlab/teachopencadd (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:23.928297+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:59:03.731914+00:00, confidence not recorded.
  - readme: https://github.com/volkamerlab/teachopencadd (fetched 2026-08-28T04:03:23.928297+00:00, sha 424baebeba90)
  - homepage: https://projects.volkamerlab.org/teachopencadd (fetched 2026-08-29T13:00:41.771631+00:00, sha 2ed111c51de3)
  - site_page: https://projects.volkamerlab.org/teachopencadd/installing.html (fetched 2026-08-29T13:00:41.781251+00:00, sha 23d89e375f3f)
  - registry_pypi: https://pypi.org/pypi/teachopencadd/json (fetched 2026-08-29T13:00:41.783250+00:00, sha 5807a3685920)
- Data as of 2026-08-30T08:39:29.467469+00:00.