AspirinCode/iupacGPT
IUPAC-based large-scale molecular pre-trained model for property prediction and molecular generation
This project helps chemists and materials scientists with designing new molecules and predicting their properties. It takes IUPAC names of chemical compounds as input and can generate novel chemical structures or predict various molecular characteristics like drug-likeness. Medicinal chemists, materials scientists, and researchers in drug discovery would find this useful for accelerating their work.
No commits in the last 6 months.
Use this if you need to generate new chemical structures or predict molecular properties using IUPAC names, especially if you prefer a more readable and semantically intuitive representation than SMILES.
Not ideal if your primary workflow relies exclusively on SMILES notation and you do not require or prefer IUPAC-based molecular representations.
Stars
11
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 03, 2025
Commits (30d)
0
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