Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Certara UK University Road Canterbury Innovation Centre Unit 43 Canterbury Kent CT2 7FG UK

Computational Drug Discovery Leiden Academic Centre for Drug Research Leiden University Einsteinweg 55 Leiden 2333 CC The Netherlands

CZ OPENSCREEN National Infrastructure for Chemical Biology Department of Informatics and Chemistry Faculty of Chemical Technology University of Chemistry and Technology Prague Technická 5 Prague A 4040 Czech Republic

Department of Human Genetics Leiden University Medical Center Einthovenweg 20 Leiden 2333ZC The Netherlands

Department of Neurosurgery Brain Tumor Center Amsterdam Amsterdam University Medical Center Cancer Center Amsterdam De Boelelaan 1117 Amsterdam 1081 HV The Netherlands

ELLIS Unit Linz and LIT AI Lab Institute for Machine Learning Johannes Kepler University Altenberger Straße 69 Linz 610101 Austria

Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 Leiden 2333 CA The Netherlands

Oncode Institute Utrecht The Netherlands

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