QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

ChemRxiv

Abstract

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 integrate 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 Contribution QSPRpred 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.

Authors 13

1. Helle W. van den Maagdenberg Aachen Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

2. Martin Šícho Aachen Leiden Academic Centre for Drug Research

   Leiden University · National Infrastructure for Chemical Biology

   Affiliation as printed

   CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology

   Leiden Academic Centre for Drug Research, Leiden University

3. David Alencar Araripe Aachen Leiden Academic Centre for Drug Research

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

   Leiden University Medical Center

4. Sohvi Luukkonen Aachen Leiden Academic Centre for Drug Research

   Leiden University · Johannes Kepler University of Linz

   Affiliation as printed

   ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University

   Leiden Academic Centre for Drug Research, Leiden University

5. Linde Schoenmaker Aachen Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

6. Michiel Jespers Aachen Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

7. Olivier J. M. Béquignon Aachen Leiden Academic Centre for Drug Research

   Leiden University · Amsterdam University Medical Centers · Cancer Center Amsterdam

   Affiliation as printed

   Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam

   Leiden Academic Centre for Drug Research, Leiden University

8. Marina Gorostiola González Aachen Leiden Academic Centre for Drug Research

   Leiden University · Oncode Institute

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

   Oncode Institute

9. Remco L. van den Broek Aachen Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University

10. Andrius Bernatavicius Aachen Leiden Academic Centre for Drug Research Leiden Institute of Advanced Computer Science

    Leiden University

    Affiliation as printed

    Leiden Academic Centre for Drug Research, Leiden University

    Leiden Institute of Advanced Computer Science, Leiden University

11. J. G. Coen van Hasselt Aachen Leiden Academic Centre for Drug Research

    Leiden University

    Affiliation as printed

    Leiden Academic Centre for Drug Research, Leiden University

12. Piet H. van der Graaf Aachen Leiden Academic Centre for Drug Research

    Leiden University

    Affiliation as printed

    Certara UK

    Leiden Academic Centre for Drug Research, Leiden University

13. Gerard J. P. van Westen Aachen Leiden Academic Centre for Drug Research

    Leiden University

    Affiliation as printed

    Leiden Academic Centre for Drug Research, Leiden University