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.
314 stars and 401 monthly downloads. Available on PyPI.
Stars
314
Forks
38
Language
Python
License
MIT
Category
Last pushed
Oct 08, 2025
Monthly downloads
401
Commits (30d)
0
Dependencies
11
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/liugangcode/torch-molecule"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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
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...
microsoft/mattersim
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.