chemotargets/assay_inspector

A Python package for diagnostic assessment of data consistency in molecular datasets

39
/ 100
Emerging

This tool helps drug discovery scientists and computational chemists rigorously assess the quality and consistency of molecular datasets, especially those related to ADME (Absorption, Distribution, Metabolism, and Excretion) properties. You input a .tsv or .csv file containing molecular SMILES, annotated values (e.g., half-life), and data source references. It generates a diagnostic report with statistical summaries, visualizations, and alerts to highlight outliers, batch effects, and discrepancies, enabling more reliable machine learning models.

No commits in the last 6 months. Available on PyPI.

Use this if you are integrating diverse molecular property datasets for machine learning and need to identify and understand inconsistencies that could compromise your model's predictive accuracy.

Not ideal if you are working with non-molecular datasets or if your primary need is data cleaning and transformation rather than diagnostic assessment of data consistency issues.

drug-discovery ADME-modeling cheminformatics preclinical-safety molecular-data-analysis
Stale 6m
Maintenance 2 / 25
Adoption 4 / 25
Maturity 25 / 25
Community 8 / 25

How are scores calculated?

Stars

8

Forks

1

Language

Python

License

MIT

Last pushed

Aug 07, 2025

Commits (30d)

0

Dependencies

19

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/chemotargets/assay_inspector"

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