In the future, we will continue to develop QSPRpred and extend its capabilities. Most notably we intend to further extend the range of available descriptors (i.e. Molfeat from datamol.io ) and models (i.e. by adding support for ensemble modelling and a wider range of neural network architectures with the help of skorch [93], a scikit-learn compatible neural network library that wraps PyTorch ). Furthermore, we will integrate QSPRpred within GenUI , which provides an accessible user interface for cheminformatics, QSAR modelling and AI-based molecular generation provided by the associated DrugEx framework . Furthermore, continued efforts are needed to teach and adhere to high standards for FAIR research practices and we hope that QSPRpred will prove to be a helpful tool to assist researchers in reproducible and transferable QSPR modelling. Therefore, with an assortment of supporting and depending packages already developed or under active development and