# PatWalters/practical_cheminformatics_tutorials

Practical Cheminformatics Tutorials

Repository: https://github.com/PatWalters/practical_cheminformatics_tutorials
Canonical: https://ross.abutalabs.com/products/practical_cheminformatics_tutorials
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-22T16:47:39+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 1627, "days_push": 11, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1300, forks 228 (observed 2026-08-28T04:04:17.500822+00:00)

## What it is
A collection of Jupyter notebook tutorials for learning practical cheminformatics using open-source tools like RDKit, datamol, and molfeat. The notebooks cover fundamentals such as SMILES/SMARTS, reaction enumeration, clustering, and working with databases like ChEMBL and BindingDB, and can be run on Google Colab or locally.

## Use cases
- learn cheminformatics with RDKit
- tutorial on SMILES and SMARTS notation
- cluster molecules with k-means or Taylor-Butina
- work with ChEMBL drug data in Python
- enumerate reactions and stereoisomers
- run cheminformatics notebooks on Google Colab
- learn pandas for chemical data analysis

## When to choose
- you want hands-on, runnable notebooks for learning cheminformatics from scratch
- you prefer open-source tools like RDKit and datamol without installing software (via Colab)
- you need practical examples of molecular clustering, reaction enumeration, or database analysis

## When to avoid
- you need a production cheminformatics library or application rather than learning material
- you require comprehensive formal coursework or certification
- you need tutorials for proprietary cheminformatics software

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, data-science, machine-learning, developer-tools
- domain: tutorials, education, data-science, chemistry
- platform: python, cross-platform
- tags: cheminformatics, rdkit, jupyter-notebooks, drug-discovery, smiles, smarts, molecular-clustering, google-colab, web

## Member repositories
- PatWalters/practical_cheminformatics_tutorials (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:17.500822+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 2026-08-30T04:52:17.381420+00:00, confidence not recorded.
  - readme: https://github.com/PatWalters/practical_cheminformatics_tutorials (fetched 2026-08-28T04:04:17.500822+00:00, sha 68628fd39fc9)
- Data as of 2026-08-30T08:39:29.467469+00:00.