lanl/minervachem
a python library for cheminformatics and machine learning
This tool helps computational chemists and materials scientists build machine learning models to predict chemical properties. You provide a list of molecules (as SMILES strings), and it generates unique 'fingerprints' that capture their structural features. These fingerprints are then used to train interpretable machine learning models, allowing you to understand which specific molecular fragments contribute to the predictions.
Use this if you need to develop highly accurate and interpretable machine learning models for predicting molecular properties based on their structure.
Not ideal if you are looking for a pre-trained model or a tool that doesn't require familiarity with Python and machine learning concepts.
Stars
17
Forks
4
Language
Python
License
—
Category
Last pushed
Feb 02, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lanl/minervachem"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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...