The introduction of machine learning to small molecule research– an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.

中文翻译：

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ChemInformatics Model Explorer (CIME)：化学模型解释的探索性分析

将机器学习引入小分子研究——一个化学家和数据科学家结合其专业知识和合作的固有多学科领域——对于提高筛选过程的效率至关重要。近年来，已经发表了许多预测药代动力学特性或生物活性的模型，化学家每天都使用这些模型来做出决定和优先考虑想法。新兴的可解释人工智能领域为理解模型背后的推理开辟了新的可能性。在小分子研究中，这意味着将化合物子结构的贡献与其预测性质联系起来，这反过来也允许确定对结果影响最大的化合物区域。然而，没有交互式可视化工具可以促进这种跨学科合作，以实现小分子机器学习模型的可解释性。为了填补这一空白，我们推出了 CIME（化学信息模型浏览器），这是一个基于 Web 的交互式系统，允许用户检查化学数据集、可视化模型解释、比较可解释性技术以及探索化合物的子组。该工具与模型无关，可以在服务器或工作站上运行。并探索化合物的亚组。该工具与模型无关，可以在服务器或工作站上运行。并探索化合物的亚组。该工具与模型无关，可以在服务器或工作站上运行。