Interactive Knowledge-based Kernel PCA for Solvent Selection

24 September 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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 datapoints, 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.

Keywords

solvent selection
green chemistry
interactive visualization
machine learning

Supplementary materials

Title
Description
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Title
Supplementary information, including some mathematical detail
Description
Mathematical description of the combined optimisation problem incorporating control points as soft constraints
Actions
Title
Solvent data and references
Description
Solvent data and source references
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