MorganCThomas/MolScore
An automated scoring function to facilitate and standardize the evaluation of goal-directed generative models for de novo molecular design
When designing new drug molecules, you need a way to automatically evaluate how well your generated compounds meet specific goals, like desired properties or activity. This tool takes your generated molecular structures (SMILES strings) and applies a customizable set of scoring functions, providing a combined score for each molecule. It's for medicinal chemists or computational drug designers who are creating new molecules using generative AI and need to assess their quality systematically.
225 stars. Available on PyPI.
Use this if you are using generative models to design novel drug candidates and need an automated, standardized way to score, evaluate, and benchmark the generated molecules against multiple desired properties.
Not ideal if you are manually designing molecules without generative models or if you only need to evaluate a single molecular property without combining multiple objectives.
Stars
225
Forks
34
Language
Python
License
MIT
Category
Last pushed
Jan 20, 2026
Commits (30d)
0
Dependencies
15
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