HUBioDataLab/SELFormer
SELFormer: Molecular Representation Learning via SELFIES Language Models
This tool helps chemists, drug discoverers, and material scientists analyze complex chemical structures. By taking chemical notations (like SMILES or SELFIES strings) as input, it generates compact, informative numerical representations of molecules. These representations are crucial for predicting molecular properties, aiding researchers in fields like drug discovery and material science.
107 stars. No commits in the last 6 months.
Use this if you need to transform chemical compound notations into robust numerical data for machine learning tasks, especially for predicting molecular properties or reactions.
Not ideal if your primary goal is basic chemical visualization or if you only work with simple molecular datasets that don't require advanced representation learning.
Stars
107
Forks
19
Language
Python
License
—
Category
Last pushed
Dec 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/HUBioDataLab/SELFormer"
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,...