MolFilterGAN

MolFilterGAN: A Progressively Augmented Generative Adversarial Network for Triaging AI-designed Molecules

Requirement

git clone https://github.com/MolFilterGAN/MolFilterGAN

cd MolFilterGAN

This project requires the following libraries.

• NumPy
• Pandas
• PyTorch > 1.2
• RDKit
• tensorboardX

Data and Trained_Models (Google_Drive)

All Data used for Training or Evaluating and the Trained_Models are available at:

https://drive.google.com/drive/folders/1uN7a5m1PmhcXfs5OuOXWPbxyF_KKuZ3A?usp=sharing

Datasets.zip contains all datasets for training MolFilterGAN

BenchmarkDatasets.zip contains all the benchmark dataset for evaluating metrics.

pretrained_G.ckpt is a pre-trained initial generator

pretrained_D.ckpt is a pre-trained initial discriminator

ADtrained_D.ckpt is an adversarial-trained discriminator

After Downloading, you can simply unzip the.zip files to get Datsets/ , BenchmarkDatasets/ and PCBA/,

and create the directions by mkdir AD_save pretrainD_save pretrainG_save then put the .ckpt files in the corresponding directions.

Finally the folder structure will look like this:

MolFilterGAN
|___AD_save 
|   |___ADtrained_D.ckpt 			# an adversarial-trained discriminator
| 
|___BenchmarkDatasetse 				# contains all the benchmark dataset for evaluating metrics.
|   |chembl-sample10000.smi
|   |___...
| 		
|___Datasets						# contains all datasets for training  MolFilterGAN
|   |Data4InitD_neg.smi
|   |___...
| 
|___PCBA
|   |ALDH1_active_T_rd_rm_less.smi
|   |___...
| 
|___pretrainD_save
|   |___pretrained_D.ckpt			# a pre-trained initial discriminator
|
|___pretrainG_save
|   |___pretrained_G.ckpt			# a pre-trained initial generator
| 
|___results							
|   |___.csv
|   |___...
| 
|___AdversarialTraining.py
| 
|___Dataset.py
...

Training a initial generator

python PretrainG.py --infile_path Datasets/Data4InitG.smi --log_path test_init_G_log --model_save_path test_init_G_save

Training a initial discriminator

python PretrainD.py --infile_path Datasets/Data4InitD.txt --log_path test_init_D_log --model_save_path test_init_D_save

Adversarial Training

python AdversarialTraining.py --infile_path Datasets/Data4InitD.txt --log_path test_AD_log --model_save_path test_AD_save --load_dir_G pretrainG_save/pretrained_G.ckpt --load_dir_D pretrainD_save/pretrained_D.ckpt

Prediction

You can easily use the trained_discrimination_models by changing the infile_path and the load_dir like:

python Prediction.py --infile_path './BenchmarkDatasets/GA-sample10000.smi' --load_dir AD_save/ADtrained_D.ckpt