MLCIL/scikit-fingerprints
Scikit-learn compatible library for molecular fingerprints and chemoinformatics
This tool helps chemists, drug discovery researchers, and materials scientists analyze and compare chemical compounds. You input chemical structures, typically as SMILES strings, and get out numerical representations (molecular fingerprints), filtered sets of molecules, or similarity scores. This is for professionals building predictive models in drug discovery, toxicology, or materials science.
352 stars.
Use this if you need to transform chemical structures into numerical data for machine learning models, apply molecular filters, or compute similarity between compounds.
Not ideal if you primarily need to visualize molecular structures or perform quantum chemistry calculations.
Stars
352
Forks
25
Language
Python
License
MIT
Category
Last pushed
Mar 27, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MLCIL/scikit-fingerprints"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...