https://github.com/gicsaw/ARAE_torch
python3
numpy
tensorflow <=1.13
RDKit
SA_Score for RDKit
https://github.com/rdkit/rdkit/tree/master/Contrib/SA_Score
git clone https://github.com/gicsaw/ARAE_SMILES
cd ARAE_SMILES
#For ZINC
python data_char_ZINC.py train
python data_char_ZINC.py test
#For QM9
python data_char_QM9.py train
python data_char_QM9.py test
#ARAE with QM9 dataset
python train_ARAE_QM9.py
#ARAE with ZINC dataset
python train_ARAE_ZINC.py
#CARAE with ZINC dataset for logP, SAS, and TPSA
python train_CARAE_logP_SAS_TPSA.py
#We prepared trained hyperparameters in save dir.
#Test for ARAE with QM9
python test_n_ARAE_QM9.py
#Test for ARAE with ZINC
python test_n_ARAE_ZINC.py
#Test for CARAE with ZINC (conditional)
python test_n_CARAE_con_logP_SAS_TPSA.py $logP $SAS $TPSA
#Test for CARAE with ZINC (unconditional)
python test_n_CARAE_uncon_logP_SAS_TPSA.py
#Molecular generation for ARAE with QM9
python gen_ARAE_QM9.py
#Molecular generation for ARAE with ZINC
python del_end_code.py out_ARAE_QM9/79
generated smiles: out_ARAE_QM9/79/smiles_gen.txt
python gen_ARAE_ZINC.py
python del_end_code.py out_ARAE_ZINC/39
generated smiles: out_ARAE_ZINC/39/smiles_gen.txt
#Molecular generation for CARAE with ZINC (conditional)
python gen_CARAE_con_logP_SAS_TPSA.py $logP $SAS $TPSA
Hong, S. H., Ryu, S., Lim, J., & Kim, W. Y. (2019). Molecular Generative Model Based On Adversarially Regularized Autoencoder. Journal of Chemical Information and Modeling.