liugangcode/torch-molecule
torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.
This package helps scientists and researchers in chemistry, biology, and materials science quickly build and deploy deep learning models for molecular discovery. You input molecular structures (like SMILES strings) and the system helps predict their properties, generate new molecules, or learn their fundamental characteristics. It's designed for anyone working with molecular data who needs to leverage advanced AI without deep machine learning expertise.
314 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a scientist or researcher looking to apply deep learning to molecular data for tasks like predicting drug properties, designing new materials, or understanding chemical reactions.
Not ideal if you need a no-code solution or if your primary focus is on fundamental research in deep learning model architectures rather than molecular applications.
Stars
314
Forks
38
Language
Python
License
MIT
Category
Last pushed
Oct 08, 2025
Commits (30d)
0
Dependencies
11
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