DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures

Background

There has been increasing interest in the use of deep neural networks for de novo design of molecules with desired properties. A common approach is to train a generative model on SMILES strings and then use this to generate SMILES strings for molecules with a desired property. Unfortunately, these SMILES strings are often not syntactically valid due to elements of SMILES syntax that must occur in pairs.

Results

We describe a SMILES-like syntax called DeepSMILES that addresses two of the main reasons for invalid syntax when using a probabilistic model to generate SMILES strings. The DeepSMILES syntax avoids the problem of unbalanced parentheses by only using close parentheses, where the number of parentheses indicates the branch length. In addition, DeepSMILES avoids the problem of pairing ring closure symbols by using only a single symbol at the ring closing location, where the symbol indicates the ring size. We show that this syntax can be interconverted to/from SMILES with string processing without any loss of information, including stereo configuration.

Conclusion

We believe that DeepSMILES will be useful, not just for those using SMILES in deep neural networks, but also for other computational methods that use SMILES as the basis for generating molecular structures such as genetic algorithms.