Songyosk/ML4SMILES
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow
This project helps chemists and materials scientists automatically predict molecular properties. You input chemical structures described by SMILES notation, and it outputs a highly accurate prediction of a target molecular property. It's designed for researchers needing to efficiently screen and evaluate new molecules.
No commits in the last 6 months.
Use this if you need to build predictive models for molecular properties based on chemical structure, and you want to automate the complex feature engineering and selection process.
Not ideal if you are looking for a pre-trained model or a simple online calculator for basic molecular properties, as this tool is for building custom predictive pipelines.
Stars
12
Forks
1
Language
Python
License
MIT
Category
Last pushed
Sep 21, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Songyosk/ML4SMILES"
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...