rnepal2/Solubility-Prediction-with-Graph-Neural-Networks
GNN, GCN, Molecular Solubility, RDKit, Cheminformatics
This project helps medicinal chemists and drug discovery scientists predict the aqueous solubility of new drug candidates. By inputting molecular structures, it outputs a classification of whether a compound is soluble, which is vital for assessing its absorption, distribution, metabolism, and excretion (ADME) properties early in development. This allows researchers to quickly evaluate and prioritize potential drug molecules.
No commits in the last 6 months.
Use this if you need to rapidly screen and classify the aqueous solubility of small organic molecules to inform early-stage drug discovery decisions.
Not ideal if you need to predict solubility for very large molecules, polymers, or inorganic compounds, as this is focused on small organic drug-like molecules.
Stars
48
Forks
18
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 02, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rnepal2/Solubility-Prediction-with-Graph-Neural-Networks"
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
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
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...