To train the SMILES corrector, a dataset with pairs of invalid and valid SMILES was created. The number of errors introduced into the valid SMILES was varied to explore the potential benefits of training with multiple errors. The best performing SMILES corrector was then used to correct invalid outputs from four de novo generation case studies: general RNN, VAE, and GAN models and a conditional RNN model. The resulting fixed molecules were then compared to an existing compound library and molecules originally generated by the four de novo models based on chemical similarity and property distributions. Lastly, the SMILES corrector was used to correct mistakes introduced into selective Aurora kinase B inhibitors to evaluate its use for the expansion of the nearby chemical space. Taken together, the experimental work presented here provides the first exploration of the potential of invalid molecular representations for de novo drug design.