yoniLc/GeometricTransformerMolecule
Transformer for End to End Molecule Property Prediction
This tool helps computational chemists and materials scientists predict properties of molecules. By inputting molecular structures, you can automatically get predictions for various chemical properties without needing deep quantum chemistry knowledge. It's designed for researchers working on drug discovery, material design, or chemical engineering who need efficient property estimation.
No commits in the last 6 months.
Use this if you need to quickly and accurately predict molecular properties directly from their geometric structures using a machine learning approach.
Not ideal if you require predictions based on explicit quantum chemistry simulations or demand interpretability rooted in specific chemical principles rather than statistical patterns.
Stars
11
Forks
1
Language
Python
License
MIT
Category
Last pushed
Jun 01, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/yoniLc/GeometricTransformerMolecule"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
rxn4chemistry/rxn-onmt-models
Training of OpenNMT-based RXN models
CTCycle/ADSMOD-Adsorption-Modeling
Streamline adsorption modeling by automatically fitting theoretical adsorption models to...
sanjaradylov/smiles-gpt
Generative Pre-Training from Molecules
lamalab-org/MatText
Text-based modeling of materials.
VectorInstitute/atomgen
Library for handling atomistic graph datasets focusing on transformer-based implementations,...