Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model

Abstract

Optimization of a synthetic reaction with respect to solvent choice and operating conditions was implemented as a machine learning-based workflow. The approach is exemplified on the case study of selection of a promising solvent to maximize the yield of a Mitsunobu reaction producing isopropyl benzoate. A solvent was defined with 15 molecular descriptors, and a library of solvent descriptors was built. The descriptors were converted into a reduced dimensionality form using an Autoencoder. Experimental yields were used to train a multilayered artificial neural network (ANN) surrogate model, which was used for the optimization and design of experiments (DoE). DoE was performed in an active learning mode to reduce the number of experiments required for reaction optimization. The final surrogate model identified 1-chloropentane as a promising solvent, which resulted in an experimental yield of 93%.