Smart mechanochemistry: optimizing amino acid acylation with one factor at a time, design of experiments and machine learning methods

Abstract

The formation of amide bonds is of major interest in organic chemistry. Several methodologies have emerged in mechanochemistry to promote this reaction by using coupling agents. Herein, the acylation of unprotected amino acids using an acyl chloride in a ball-mill is described with different optimization processes. Indeed, the optimization of reaction conditions is part of every development of a new synthetic pathway. However, depending on the method which is used, the number of experiments to carry out can increase exponentially. Three different optimization methods were compared in the acylation of amino acids: One Factor at a Time (OFAT), Design of Experiments (DoE) and Bayesian Optimization (BO). The strengths and limitations of each methodology are highlighted providing new insights and an optimized practical amidation method taking into account the sustainability of this chemistry.