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dataset
model
save
LICENSE
README.md
data_char_QM9.py
data_char_ZINC.py
gen_ARAE_QM9.py
gen_ARAE_ZINC.py
gen_CARAE_con_logP_SAS_TPSA.py
test_n_ARAE_QM9.py
test_n_ARAE_ZINC.py
test_n_CARAE_con_logP_SAS_TPSA.py
test_n_CARAE_uncon_logP_SAS_TPSA.py
train_ARAE_QM9.py
train_ARAE_QM90.py
train_ARAE_ZINC.py
train_CARAE_logP_SAS_TPSA.py

README.md

ARAE for molecular generation with SMILES representation

Prerequisite:

python3

numpy

tensorflow

RDKit

SA_Score for RDKit

https://github.com/rdkit/rdkit/tree/master/Contrib/SA_Score

Download:

git clone https://github.com/gicsaw/ARAE_SMILES

Training data preparation :

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

training:

#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

Test:

#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:

#Molecular generation for ARAE with QM9

python gen_ARAE_QM9.py

#Molecular generation for ARAE with ZINC

python gen_ARAE_ZINC.py

#Molecular generation for CARAE with ZINC (conditional)

python gen_CARAE_con_logP_SAS_TPSA.py $logP $SAS $TPSA

References:

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