  Simplified Molecular Input Line Entry System (SMILES) is a single line text
representation of a unique molecule. One molecule can however have multiple
SMILES strings, which is a reason that canonical SMILES have been defined,
which ensures a one to one correspondence between SMILES string and molecule.
Here the fact that multiple SMILES represent the same molecule is explored as a
technique for data augmentation of a molecular QSAR dataset modeled by a long
short term memory (LSTM) cell based neural network. The augmented dataset was
130 times bigger than the original. The network trained with the augmented
dataset shows better performance on a test set when compared to a model built
with only one canonical SMILES string per molecule. The correlation coefficient
R2 on the test set was improved from 0.56 to 0.66 when using SMILES
enumeration, and the root mean square error (RMS) likewise fell from 0.62 to
0.55. The technique also works in the prediction phase. By taking the average
per molecule of the predictions for the enumerated SMILES a further improvement
to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
