Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions
Ajnabiul Hoque, Raghavan B. Sunoj
Abstract
Molecular descriptors from a mechanistically important metal–ligand–substrate complex are used for the first time to build a Deep Neural Network (DNN) model to predict % ee. Accurate and chemically meaningful predictions could be obtained.
Topics & Concepts
CatalysisSubstrate (aquarium)Ligand (biochemistry)Artificial neural networkDeep neural networksDeep learningChemistryArtificial intelligenceComputer scienceCombinatorial chemistryOrganic chemistryBiologyReceptorBiochemistryEcologyMachine Learning in Materials ScienceComputational Drug Discovery MethodsAsymmetric Hydrogenation and Catalysis