aksub99/molecular-vae
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
This tool helps computational chemists and materials scientists rapidly design new chemical compounds with desired properties. You input a dataset of known molecules, and it outputs novel molecular structures that could potentially have improved characteristics for various applications. It's ideal for researchers in drug discovery or materials science looking to explore chemical space more efficiently.
No commits in the last 6 months.
Use this if you need to generate new molecular structures that are optimized for specific properties, beyond what's already known or easily synthesizable.
Not ideal if you're looking for a simple simulator for existing molecules or a tool to analyze chemical reactions.
Stars
71
Forks
15
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 31, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aksub99/molecular-vae"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...