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

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%.