MedGAN

Optimized Generative Adversarial Network with Graph Convolutional Networks for Novel Molecule Design

Instructions

ZINC15 dataset

• two subsets of ZINC15 data
• 100k random molecules stored in archive folder

1. MedGAN_WGAN-GP

• Create a logs folder for TensorBoard metrics (one for each model).
• Create a training_checkpoints directory to store each training step (one for each model).
• Create a training_model directory to store the model saved after training (one for each model).
• Create summaries for the generator and discriminator in both .txt and .png formats (one for each model).

2. MedGAN_generator

• Generate molecules for each model.
• Compute model performance metrics.

3. MedGAN_drug-likeness_analysis

• Use the toxicity prediction model to make predictions on the generated molecules.
• Utilize sascorer.py to apply the Erl algorithm on synthetic accessibility.
• Calculate Lipinski's Rule of Five for the generated molecules.