| --- |
| license: apache-2.0 |
| tags: |
| - molecular-property-prediction |
| - drug-discovery |
| - pytorch |
| - biology |
| - chemistry |
| datasets: |
| - OGB/ogbg-molhiv |
| metrics: |
| - accuracy |
| model-index: |
| - name: ODE MolPredictor |
| results: |
| - task: |
| type: binary-classification |
| name: HIV Activity Prediction |
| dataset: |
| name: ogbg-molhiv |
| type: OGB/ogbg-molhiv |
| metrics: |
| - type: accuracy |
| value: 0.975 |
| name: Validation Accuracy |
| --- |
| |
| # ODE MolPredictor — Molecular Property Prediction |
|
|
| **By Llewellyn Systems Inc** | Part of the [ODE Science](https://www.llewellynsystems.com/products/ode-science) platform |
|
|
| ## Model Description |
| MLP-based molecular property predictor trained on OGB ogbg-molhiv dataset. Predicts HIV activity from molecular graph statistics. |
|
|
| ## Performance |
| - **97.5% validation accuracy** on ogbg-molhiv binary classification |
| - Trained on RTX A4000 GPU |
| - 30 input features (node statistics, edge statistics, graph structure) |
|
|
| ## Architecture |
| ``` |
| Linear(30, 256) → ReLU → BN → Dropout(0.3) |
| Linear(256, 128) → ReLU → BN → Dropout(0.2) |
| Linear(128, 64) → ReLU |
| Linear(64, 1) → Sigmoid |
| ``` |
|
|
| ## Usage |
| ```python |
| import torch |
| model = MolPredictor(30) |
| model.load_state_dict(torch.load("mol_predictor_best.pt")) |
| model.eval() |
| ``` |
|
|
| ## Training |
| - Dataset: OGB ogbg-molhiv (32,901 molecules) |
| - Epochs: 100 |
| - Optimizer: Adam (lr=1e-3, weight_decay=1e-5) |
| - GPU: NVIDIA RTX A4000 |
| |
| ## About ODE Science |
| ODE Science is an AI-powered scientific research platform by Llewellyn Systems Inc, featuring molecular modeling, protein analysis, medical imaging, and quantum computing capabilities. |
| |
| [Website](https://www.llewellynsystems.com) | [ODE Science](https://www.llewellynsystems.com/products/ode-science) |
| |
