Interactive Knowledge-Based Kernel PCA for Solvent Selection

Selecting more sustainable solvents is a crucial component to mitigating the environmental impacts of chemical processes. Numerous tools have been developed to address this problem within the pharmaceutical industry, employing data-driven approaches such as multidimensional scaling or principal component analysis (PCA). Interactive knowledge-based kernel PCA is a variant of PCA that allows users to shape 2D solvent maps by defining the positions of data points, imparting expert knowledge that was not included in the original descriptor set. We have applied interactive PCA to the task of solvent selection and present an intuitive interface that is integrated into AI4Green, an electronic laboratory notebook that encourages sustainable chemistry. A set of evidence-based user guidelines were developed and used in combination with the interactive PCA to identify four potential solvent substitutions for an example thioesterification reaction.筛选更具可持续性的溶剂，是缓解化工过程环境影响的关键环节。制药行业内已开发出诸多工具以解决该类问题，这些工具采用多维标度法、主成分分析（Principal Component Analysis，PCA）等数据驱动方法。交互式知识基核主成分分析（Kernel PCA）是主成分分析的一类变体，它允许用户通过定义数据点的位置来构建二维溶剂图谱，从而引入原始描述符集中未涵盖的专家知识。我们将该交互式知识基核主成分分析应用于溶剂筛选任务，并展示了一款集成至AI4Green的直观界面——AI4Green是一款致力于推动可持续化学发展的电子实验记录本。我们开发了一套基于证据的用户指南，并将其与该交互式知识基核主成分分析结合使用，为一个硫酯化反应示例筛选出四种潜在的溶剂替代方案。