Data augmentation for physico-chemical properties, yes please!

Are you tired of hearing that deep learning needs more data and that physico-chemical data sets are still scarce? Check out our latest publication Kimber, 2021 for some augmentation strategies you can apply to improve your predictions when training deep neural networks using SMILES.

You can even make predictions for novel compounds using the command-line interface. The code is open-source and available on GitHub at https://github.com/volkamerlab/maxsmi.