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 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.

中文翻译：

---

QSPRpred：灵活的开源定量结构-属性关系建模工具


构建可靠且稳健的定量结构-属性关系 （QSPR） 模型是一项具有挑战性的任务。首先，需要获取、分析和整理实验数据。其次，可用方法的数量不断增长，评估不同的算法和方法可能很困难。最后，研究人员面临的最后一个障碍是确保其模型的可重复性并促进其可转移到实践中。在这项工作中，我们介绍了 QSPRpred，这是一个用于分析生物活性数据集和 QSPR 建模的工具包，它试图解决上述挑战。QSPRpred 的模块化 Python API 使用户能够使用大量预实现的组件直观地描述建模工作流程的不同部分，但也以“即插即用”的方式集成自定义实现。QSPRpred 数据集和模型是可直接序列化的，这意味着它们可以在训练后轻松复制并投入运行，因为模型与所有必需的数据预处理步骤一起保存，以便直接从 SMILES 字符串对新化合物进行预测。QSPRpred 的通用特性还通过包含对多任务和蛋白质化学计量学建模的支持来证明。该软件包有广泛的文档记录，并附带大量教程来帮助新用户。在本文中，我们描述了 QSPRpred 的所有功能，并进行了一个小型基准测试案例研究，以说明如何利用不同的组件来比较一组不同的模型。QSPRpred 是完全开源的，可在 https://github.com/CDDLeiden/QSPRpred 上获得。 科学贡献QSPRpred 旨在提供一个复杂但全面的 Python API 来执行 QSPR 建模中遇到的所有任务，从数据准备和分析到模型创建和模型部署。与类似软件包相比，QSPRpred 提供了更广泛、更详尽的功能，并与许多流行的软件包集成，这些软件包也超越了 QSPR 建模。QSPRpred 的重大贡献还在于其自动化和高度标准化的电子监管方案，这显著提高了模型的可重复性和可转移性。