Towards an expert system for enantioseparations: induction of rules using machine learning

Abstract A commercially available machine induction tool was used in an attempt to automate the acquisition of the knowledge needed for an expert system for enantioseparations by high performance liquid chromatography using Pirkle-type chiral stationary phases (CSPs). Various rule-sets were induced that recommended particular CSP chiral selectors based on the structural features of an enantiomer pair. The results suggest that the accuracy of the optimal rule-set is 63% ± 3%, which is more than ten times greater than the accuracy that would have resulted from a random choice.

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