Exploring Spectrum‐based Molecular Descriptors for Reaction Performance Prediction

Abstract

Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition-based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pdcatalyzed Buchwald-Hartwig cross-coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training.

Prediction of chemical reaction yields with large-scale multi-view pre-training

“…Chemical reactions can be perceived as sequences or collections of molecules. Therefore, the traditional practice of concatenating molecular fingerprints or descriptors at the molecule level is commonly employed [ 15 – 18 ]. However, this concatenation approach is typically suitable only for reactions with a fixed number of molecules, posing limitations on their ability to generalize to downstream tasks.…”

Prediction of Reaction Performance by Machine Learning Using Streamlined Features: NMR Chemical Shifts as Familiar Descriptors

“…Recently, remarkable accomplishments have been reported, and predictive models for various organic reactions were gradually generated. [9][10][11][12][13][14][15][16][17][18][19] As representative examples, the Denmark and the Sigman groups applied the ML approach to predict the enantioselectivity of asymmetric imine addition, [9,13] and Doyle's work on the Buchwald-Hartwig crosscoupling showed extrapolative prediction of reaction yield. [12] These adaptations of ML in synthetic organic chemistry make reaction design more efficient by reducing time-and resource-consuming trial-and-error routines.…”

“…These ML models show a powerful predictive ability for chemical yields as well as stereoselectivities ( Scheme 1). Recently, remarkable accomplishments have been reported, and predictive models for various organic reactions were gradually generated [9–19] . As representative examples, the Denmark and the Sigman groups applied the ML approach to predict the enantioselectivity of asymmetric imine addition, [9,13] and Doyle's work on the Buchwald–Hartwig cross‐coupling showed extrapolative prediction of reaction yield [12] .…”