Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers

Abstract

Detection of stereoisomers of carbohydrates with molecular resolution, a challenging goal analysts desire to achieve, is key to the full development of glycosciences. Despite the promise that analytical techniques made, including widely used nuclear magnetic resonance and mass spectrometry, high throughput de novo carbohydrate sequencing remains an unsolved issue. Notably, while next-generation sequencing technologies are readily available for DNA and proteins, they are conspicuously absent for carbohydrates due to the immense stereochemical and structural complexity inherent in these molecules. In this work, we report a novel computational technique that employs quantum tunneling coupled with artificial intelligence to detect complex carbohydrate anomers and stereoisomers with excellent sensitivity. The quantum tunneling footprints of carbohydrate isomers show high distinguishability with an in-depth analysis of underlying chemistry. Our findings open up a new route for carbohydrate sensing, which can be seamlessly integrated with next-generation sequencing technology for real-time analysis.