The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contribution We provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models.

中文翻译：

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MolCompass：用于化学空间导航和 QSAR/QSPR 模型视觉验证的多功能工具


数据的指数级增长对人类来说是一个挑战，因为他们分析数据的能力是有限的。特别是在化学领域，需要能够以方便的图形方式可视化分子数据集的工具。我们提出了一个新的、即用型、多功能工具和开源框架，用于可视化和导航化学空间。该框架遵循低代码/无代码 (LCNC) 范例，提供 KNIME 节点、基于 Web 的工具和 Python 包，使其可供广泛的化学信息学社区使用。 MolCompass 框架的核心技术采用预先训练的参数化 t-SNE 模型。我们演示了该框架如何适用于化学空间的可视化和二元分类 QSAR/QSPR 模型的视觉验证，揭示其弱点并识别模型悬崖。该框架的所有部分均在 GitHub 上公开提供，为广大科学界提供了可访问性。科学贡献 我们提供一套开源、即用型的化学空间可视化工具。这些工具可以为化学家分析化合物数据集和 QSAR/QSPR 模型的视觉验证提供富有洞察力的工具。