Feed Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction

31 January 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Density functional theory (DFT) has become a popular method to model transition state (TS) energies to predict enantioselectivity, but the associated errors present challenges. Machine learning has emerged as a powerful tool to model enantioselectivity but generally requires large datasets for training. Herein, we describe the development of a feed forward neural network for predicting enantioselectivity of the Negishi cross-coupling reaction with Boehringer Ingelheim (BI)-type phosphines. The selectivity predicted from DFT TS energies is upgraded through the neural network based on input features including geometries, electron population, and dispersive interactions. This new approach to modeling enantioselectivity is compared to conventional approaches, including exclusive use of DFT energies, and data science approaches using features from ligands or ground states with simple neural network architectures.


Machine Learning
Neural Network


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.