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.