A Structure-Based Platform for Predicting Chemical Reactivity

Abstract

Despite their enormous potential, machine learning methods have only found limited application in predicting reaction outcomes, as current models are often highly complex and, most importantly, are not transferrable to different problem sets. Herein, we present the direct utilization of Lewis structures in a machine learning platform for diverse applications in organic chemistry. Therefore, an input based on multiple fingerprint features (MFF) as a universal molecular representation was developed and used for problem sets of increasing complexity: First, molecular properties across a diverse array of molecules could be predicted accurately. Next, reaction outcomes such as stereoselectivities and yields were predicted for experimental data sets that were previously evaluated using (complex) problem-oriented descriptor models. As a final application, a systematic high-throughput data set showed good correlation when using the MFF model, which suggests that this approach is general and ready for immediate adoption by chemists.