This is a playground repository. This README.md was partly generated by Claude Sonnet 4.6.
Predicting glass transition temperature (Tg) of polymers from SMILES using Graph Neural Networks.
Measuring polymer properties experimentally is costly and time-consuming. ML-based prediction allows screening thousands of candidates in silico before any synthesis — drastically accelerating materials discovery.
GNNs are particularly suited for polymers: unlike fingerprint-based models (Random Forest, SVM), GNNs operate directly on the molecular graph and learn structural patterns automatically, without manual feature engineering.
Predicting Tg before synthesis has direct industrial impact: it guides the design of high-performance polymers for applications in aerospace, electronics, and packaging.
SMILES → RDKit graph → PyTorch Geometric Data object
↓
GCNConv (9 → 128) + ReLU
↓
GCNConv (128 → 64) + ReLU
↓
global_mean_pool
↓
Linear (64 → 1) → Tg (K)
Node features (9): atomic number, degree, formal charge, is in ring, is aromatic, hybridization, total Hs, is in ring of size 5, is in ring of size 6
Dataset: PolyMetriX CuratedGlassTempDataset — 7 367 labeled polymers Split: 80% train / 10% val / 10% test
| Metric | Value |
|---|---|
| RMSE (test) | ~64 K |
| MAE (test) | ~50 K |
Baseline model — not yet optimized. Further improvements planned (deeper architecture, edge features, PolyBERT comparison).
git clone https://github.com/Pablo-R0/MolProPreNet
cd MolProPreNet
conda create -n molproprenet python=3.10
conda activate molproprenet
pip install -r requirements.txt# To be updated- PolyMetriX : github.com/lamalab-org/PolyMetriX
- PyTorch Geometric : pytorch-geometric.readthedocs.io