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README.md

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+---
+language: en
+tags:
+  - graph-neural-network
+  - molecular-property-prediction
+  - quantum-chemistry
+  - cheminformatics
+  - pytorch
+  - SMILES
+  - attention-mechanism
+  - GNN
+license: mit
+library_name: pytorch
+datasets:
+  - QuantumChem/QuantumChem_200k
+metrics:
+  - mae
+  - r2
+---
+
+# MSGAT: Multi-Scale Graph Attention Network
+
+A lightweight graph neural network (**242K parameters**) for predicting 10 quantum-chemical properties from molecular SMILES strings.
+
+## Model Details
+
+- **Architecture**: Edge-aware multi-head attention + MPNN + cross-scale fusion
+- **Parameters**: 242,407
+- **Hidden dim**: 64, 4 attention heads, 3 layers
+- **Node features**: 58-dim (atomic number, degree, charge, Hs, hybridization, aromaticity)
+- **Bond features**: 12-dim (bond type, conjugation, ring, stereo)
+- **Training data**: [QuantumChem/QuantumChem_200k](https://huggingface.co/datasets/QuantumChem/QuantumChem_200k)
+
+## Properties Predicted
+
+| Property | Unit | MAE | R² |
+|---|---|---|---|
+| Sigma at 780 nm | GM | 10.10 | 0.953 |
+| Max sigma | GM | 10.19 | 0.957 |
+| ISC energy | eV | 0.0055 | 0.933 |
+| Toxicity score | — | 0.017 | 0.924 |
+| SA score | — | 0.0069 | 0.967 |
+| Boiling point | °C | 5.77 | 0.984 |
+| logP | — | 0.057 | 0.993 |
+| Aromaticity | — | 0.0077 | 0.998 |
+| Solubility | ug/ml | 71,953 | 0.053 |
+| Molecular weight | g/mol | 1.61 | 0.806 |
+
+Mean R² across 9/10 properties (excl. solubility): **0.946**
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+
+# Load the model
+model_state = torch.load("msgat_model.pt", weights_index=None)
+norm_stats = torch.load("norm_stats.pt")
+
+# Reconstruct model architecture (see model.py in the repo)
+from model import create_model  # clone https://github.com/devansh0703/MSGAT
+model = create_model()
+model.load_state_dict(model_state)
+model.eval()
+
+# mean/std for inverse transform (shape: [10])
+mean = norm_stats["mean"]
+std = norm_stats["std"]
+```
+
+### Inverse normalization
+
+The model outputs z-score normalized predictions. To get raw values:
+
+```python
+# solubility uses log1p before normalization — must invert with expm1
+raw = preds * std + mean
+sol_idx = 8  # solubility index in active props
+raw[:, sol_idx] = torch.expm1(raw[:, sol_idx])
+```
+
+## Files
+
+| File | Description |
+|---|---|
+| `msgat_model.pt` | Trained model state dict (242K params) |
+| `norm_stats.pt` | Z-score normalization stats (mean, std) for the 10 active properties |
+
+## Training
+
+```bash
+git clone https://github.com/devansh0703/MSGAT
+cd MSGAT
+pip install torch rdkit-pypi datasets pandas tqdm matplotlib
+python train.py  # train/val split
+python train_final.py  # full data retrain
+```
+
+## Citation
+
+```bibtex
+@article{raulo2025msgat,
+  title={MSGAT: Multi-Scale Graph Attention Network for Efficient Molecular Property Prediction},
+  author={Raulo, Devansh},
+  year={2025}
+}
+```
+
+## Acknowledgements
+
+Dataset: [QuantumChem/QuantumChem_200k](https://huggingface.co/datasets/QuantumChem/QuantumChem_200k) by Zeng et al.

msgat_model.pt

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norm_stats.pt

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