General Reaction Conditions via Data-driven Optimisation

Abstract

General reaction conditions are a long-standing goal in chemical synthesis, as such conditions facilitate library synthesis and a broad substrate scope. However, despite their importance, the generality of reaction conditions is mostly an afterthought when reaction conditions are optimised. Considering multiple substrates during reaction condition optimisation alleviates this problem and enables the identification of conditions that work well for multiple substrates. Inspired by data-driven optimisation techniques for one model substrate, machine learning based strategies have also been proposed to optimise reactions towards general reaction conditions. In this work, we describe recent algorithmic advances in this domain, including our state-of-the-art algorithm. This algorithm is also available as an easy-to-use website to allow experimental chemists to use it without code.