<p>SMILES
randomization, a form of data augmentation, has previously been shown to
increase the performance of deep learning models compared to non-augmented
baselines. Here, we propose a novel data augmentation method we call “Levenshtein
augmentation” which considers local SMILES sub-sequence similarity between
reactants and their respective products when creating training pairs. The performance
of Levenshtein augmentation was tested using two state of the art models -
transformer and sequence-to-sequence based recurrent neural networks with
attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and
conventionally SMILES randomization augmented data when used for training of
baseline models. Furthermore, Levenshtein augmentation seemingly results in
what we define as <i>attentional gain </i>– an
enhancement in the pattern recognition capabilities of the underlying network
to molecular motifs.</p>
Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks
