This project is the first comprehensive investigation of the applicability of deep learning methods to correct invalid sequences in de novo drug design. Commonly used SMILES-based generators produce some percentage of invalid outputs and here it was shown that invalid SMILES are not merely useless or something to avoid. This study has found that a transformer network trained on synthetic mistakes is able to fix more than 60 % of invalid SMILES originating from different molecular generators with different distributions of error types. SMILES correctors trained on input sequences with multiple errors demonstrated a higher performance. This study also showed that the pretrained SMILES corrector generates novel molecules that follow the same distribution as the original generators and/or set of molecules, hence demonstrating that the SMILES corrector can independently be used for the exploration of the nearby space of molecules of interest.