| --- |
| 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. |
|
|
