In silico reaction screening with difluorocarbene for N-difluoroalkylative dearomatization of pyridines

Abstract

Quantum chemical calculations are mainly regarded as a method for mechanistic studies in organic chemistry, whereas their use for the simulation of unknown reactions could greatly assist in reaction development. Here we report a strategy for developing multicomponent reactions on the basis of the results of computational reaction simulations. In silico screening of multicomponent reactions with difluorocarbene using the artificial force induced reaction method suggested that cycloadditions between an azomethine ylide and a variety of coupling partners would proceed to generate the corresponding α,α-difluorinated N-heterocyclic compounds. The predicted reaction was successfully realized experimentally, leading to a multicomponent N-difluoroalkylative dearomatization of pyridines involving a pyridinium ylide-mediated 1,3-dipolar cycloaddition with a diverse range of electrophiles such as aldehydes, ketones, imines, alkenes and alkynes. Moreover, the performance of the cycloaddition could be explained by comparing the energy barrier of the desired pathway with that of the competitive undesired pathway, which was also identified by the artificial force induced reaction search.

On accelerating reaction optimization using computational Gibbs energy barriers: A numerical consideration utilizing a computational dataset

“…[40][41][42][43][44][45][46][47] There also are methods to directly determine the reaction yield through automatic reaction path network explorations considering formations of various resting and byproduct states. [63,64] Evaluating the reactivity accurately considering an entire reaction profile or reaction path network could often be more timeconsuming than a single experiment. On the other hand, focusing only on a few elementary steps could mislead BO due to a wrong kinetic description based on an oversimplified model.…”

Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery

“…In silico strategies are experiments that develop virtually using any computation machine, for example, digital computers. [ 38 ] Quantum chemistry simulations offer highly accurate models; however, even moderate‐sized molecules (100 electrons) surpass current digital computational resources rendering them infeasible to compute. [ 39 ] To circumvent these limitations, other computational strategies have been developed, most noticeably AI methods, for example, by introducing graph‐based neural network architectures, Qiao et al have predicted energy solutions to the Schrödinger equation matching the accuracy of density‐functional theory (DFT) while reducing the computational cost by 1000X.…”

Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets

“…Indeed, using QCaRA, we successfully searched for reactants of several useful organic molecules. Difluoroglycine (10 atoms), α-amino nitrile (19 atoms), and α-formyloxyamide (20 atoms) have already been computationally retrosynthesized into reasonable reactants via the carboxylation of ammonium ylide, the Strecker reaction of acetone, and the Passerini reaction of formaldehyde, respectively. In particular, one of the reactant combinations proposed by QCaRA for difluoroglycine, which consisted of a tert -amine, difluorocarbene, and CO 2 , was tested experimentally to afford a difluoroglycine derivative in high yields .…”