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eosquality

PyPIInfrastructure

No liveness check has reached it yet; it is registered on GitHub and PyPI and has been in the index since 9 Oct 2026. How this is checked

eosquality is an open-source Python package for assessing query data quality against a fitted reference population. It supports applicability-domain and quality-assessment workflows for cheminformatics researchers and data scientists.

Inferred · not functionally tested

Open SourceGPL-3.0CLISelf-hosted
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1star
4features
2026since

Overview

4 features

Purpose: Assessing whether query cheminformatics data is representative and within the applicability domain of a reference population.

Inferred · not functionally tested

Audience: cheminformatics researchers and data scientists

Inferred · not functionally tested

Functions: data_extraction

Inferred · not functionally tested

Interfaces: API: unknown · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: GPL-3.0 · platforms: CLI · deployment: cli, self_hosted

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org · github.com. These links do not verify the individual claims.

eosquality is an Other data science & ML project. Inferred · not functionally tested: It focuses on assessing whether query cheminformatics data is representative and within the applicability domain of a reference population. Inferred · not functionally tested: eosquality is an open-source project aimed at cheminformatics researchers and data scientists. Basis unknown · not verified: eosquality is open source under the GPL-3.0 license. Basis unknown · not verified: It runs on the command line, and it can be self-hosted.

It is developed by Ersilia Open Source Initiative, and it first shipped in 2026. The project is developed in the open on GitHub with 74 commits in the last 90 days. Inferred · not functionally tested: Key capabilities include reference population assessment, query data quality, and applicability-domain analysis.

Summary written by a language model from the project’s public pages.

Tasks: Inferred · not functionally tested

  • Reference population assessment
  • Query data quality
  • Applicability-domain analysis
  • Cheminformatics workflows

Topics: Inferred · not functionally tested

Tags
cheminformaticsapplicability-domaindata-qualityreference-population

JSON profile · Text profile · Access guide

Built with & integrations

Written with
Claude Code
Runs on
CLISelf-hosted
Written with — evidence
Claude Code
commit be198cf8e71f · since Oct 2026

Trust & compliance

License
GPL-3.0
Public signals
HTTPSOpen SourceFree tierGitHub · ★ 1Active maintenance

Indexing history

1

What PulseGate has recorded for this listing

  1. Indexed9 Oct · 13:05 UTC
    eosquality seen via PyPI Bulk Enumerator
    Source: PyPI Bulk Enumerator · Open

Frequently asked questions about eosquality

What is eosquality?
Inferred · not functionally tested: Eosquality focuses on assessing whether query cheminformatics data is representative and within the applicability domain of a reference population. It is catalogued under Other data science & ML on PulseGate.
Who is eosquality for?
Inferred · not functionally tested: eosquality is an open-source project built for cheminformatics researchers and data scientists.
Does eosquality have a free plan?
Basis unknown · not verified: Yes — eosquality is open source under the GPL-3.0 license and free to use.
What platforms does eosquality run on?
Basis unknown · not verified: eosquality runs on the command line. It can also be self-hosted.
Is eosquality still active?
The GitHub repository shows 74 commits in the last 90 days.
What are alternatives to eosquality?
Similar projects tracked by PulseGate include fxtensor-bayesnet, AutoTinker, and EMTIOP.fxtensor-bayesnetAutoTinkerEMTIOP
Who makes eosquality?
eosquality is developed by Ersilia Open Source Initiative.
When did eosquality launch?
eosquality first shipped in 2026.

Also in Other data science & ML

Same category — not a similarity match