  We present here a combination of two networks, Recurrent Neural Networks
(RNN) and Temporarily Convolutional Neural Networks (TCN) in de novo reaction
generation using the novel Reaction Smiles-like representation of reactions
(CGRSmiles) with atom mapping directly incorporated. Recurrent Neural Networks
are known for their autoregressive properties and are frequently used in
language modelling with direct application to SMILES generation. The relatively
novel TCNs possess similar properties with wide receptive field while obeying
the causality required for natural language processing (NLP). The combination
of both latent representations expressed through TCN and RNN results in an
overall better performance compared to RNN alone. Additionally, it is shown
that different fine-tuning protocols have a profound impact on generative scope
of the model when applied on a dataset of interest via transfer learning.
