Of the more recent ML-based approaches to de novo molecule generation, RNNs were one of the first to appear with the seminal approaches being published ~ 5 years ago. The first by Segler et al. who fine-tuned an RNN on molecules of biological interest to generate molecules containing similar properties de novo. The second by Olivecrona et al. who instead used RL to update the RNN to generate molecules de novo that maximized predicted properties (e.g., predicted bioactivity of molecules). These results were obtained by representing molecules using the SMILES language which emulates the RNN's designed application for use in natural language processing . When trained on a large dataset of SMILES (>10 5 ), an RNN can predict the next symbol in a sequence conditional upon previously seen symbols. Thus, by supplying a start symbol, new symbols can be sampled from the probability distribution corresponding to the next symbol (output by the RNN), which is then recursively fed back into the network resulting in de novo molecules.