metadata
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 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
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.