Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.

ABSTRACT: Molecular machine learning (ML) has gained considerable attention in recent years. Developing ML algorithms for chemical reaction prediction is a formidable task, due to the small-sized reaction data it often presents, besides the sparsity and skewed distribution. While previous ML studies offered effective predictions on known reactions, efforts in using deep generative models for guiding new reactions and their prospective validation are rare. We harness both predictive and explorative abilities of deep learning on an important catalytic asymmetric β-C(sp³)-H activation reaction, consisting of 220 experimentally reported examples that differs primarily in terms of the substrate, catalyst, and coupling partner. A transfer learning approach using a chemical language model, pretr

SUBMITTER: Hoque A 

PROVIDER: S-EPMC12184194 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature