PyRank

lbvslitmus

A modern, extendible platform for ligand-based virtual screening benchmarking. Automates data downloading, preprocessing, ML training, evaluation, and statistical analysis. Supports multiple datasets, molecular embeddings (fingerprints and pretrained neural networks), similarity search, and ML classifiers for identifying bioactive compounds.
PyPI
More links
Download Stats
Monthly downloads
117
Weekly downloads
42
GitHub stars
-

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
1.2.1
Requires Python
<3.14,>=3.11
Licence
MIT License
Last Release
8 months ago
Author
Kacper Szot, Maciej Nowakowski, Kinga Bunkowska, Jakub Adamczyk, AGH ML & Chemoinformatics Group
First Release
January 8, 2026
Total Releases
2
Python Versions
3
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score based on PyPI data only - no linked source repository
N/A
Health Score
Not available
requires GitHub data
How the maintenance status was determined
Last release
245 days ago
Monthly downloads
117
Active
Activity within 30 days
Stable
Activity within 31-180 days
• Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
117
Stars
Unknown
Stars per 1k downloads
—
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
6
Month daily avg
4
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
<3.14,>=3.11
Cutting Edge
Minimum supported version is 3.14 or newer
• Modern
Minimum supported version is 3.11 to 3.13
Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT License
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
245 days ago
Total releases
2
Releases/year
2.6
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
Unknown
Monthly downloads
117
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
Balanced
Enough contributors for the package's popularity
• Unknown
No contributor data available
Contributor count is missing — this usually indicates the package has no linked GitHub repository.
Engagement
0
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
-
contrib / forks
Stale Release
No
 
PR Velocity
-
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

No OS data available

Python Versions

No Python version data available

Top Countries

1. US US
2
2. DE DE
1

Weekly Downloads

GitHub Stars

Dependencies

17
4 optional dependencies

Used By

0
Not used by any tracked packages

Repository details

Forks
-
Issues
-
Last Commit
-
Contributors
-
Open PRs
-
Archived
-

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Sources and methodology