# De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2310.17341 · HTML: https://mlsanity.com/paper/2310.17341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network](https://mlsanity.com/paper/1709.04555/) (2017)
- [Very Deep Convolutional Networks for Text Classification](https://mlsanity.com/paper/1606.01781/) (2016)
- [GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks](https://mlsanity.com/paper/1909.04825/) (2019)

## Abstract (arXiv)

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.