PySMILESUtils – Enabling deep learning with the SMILES chemical language

29 June 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship modelling, forward reaction prediction and single-step retrosynthetic planning as examples. PySMILESUtils aims to enable these tasks by providing readyto- use and adaptable Python classes for tokenization, augmentation, dataset, and dataloader creation. Classes for handling datasets larger than memory and speeding up training by minimizing padding are also provided. The framework subclasses PyTorch dataset and dataloaders but should be adaptable for other deep learning frameworks. The project is open-sourced with a permissive license and made available at GitHub:

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