no / The requirement to efficiently store and process SMILES data used in Chemoinformatics creates a demand for efficient techniques to compress this data. General-purpose transforms and compressors are available to transform and compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. We develop a transform specific to SMILES data that can be used alongside other general-purpose compressors as a preprocessor and post-processor to improve the compression of SMILES data. We test our transform with six other general-purpose compressors and also compare our results with another transform on our SMILES data corpus, we also compare our results with untransformed data.