ArticleChemical science2020
Predicting glycosylation stereoselectivity using machine learning.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Bridging Chemical Literature and Molecular Graphs: LLM-Augmented Enantioselectivity Prediction for Iridium-Catalyzed Asymmetric C-H Borylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- p-nitrobenzyloxycarbonyl protective group as key to automated glycan assembly of neutral human milk oligosaccharides.Nature communications · 2025Article
- Acceptor-Adaptive Automated Glycosylation Optimization for Automated Glycan Assembly.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Rapid Stereochemical Analysis of Glycosylations in Flow by Ion Mobility Spectrometry.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Modeling of the Carbohydrate Oxacarbenium Ionic Intermediates of Glycosylation Reactions with Explicit Account for Protective Group Effects.ACS omega · 2025Article
- Stereoelectronic Effect of Protecting Groups on the Stability of Galactosyl Donor Intermediates.Molecules (Basel, Switzerland) · 2025Article
- Acetal Substitution Reactions: Stereoelectronic Effects, Conformational Analysis, Reactivity vs. Selectivity, and Neighboring-Group Participation.Synlett : accounts and rapid communications in synthetic organic chemistry · 2024Article
- Predicting the stereoselectivity of chemical reactions by composite machine learning method.Scientific reports · 2024Article
- Bimodal Glycosyl Donors as an Emerging Approach Towards a General Glycosylation Strategy.Chemistry (Weinheim an der Bergstrasse, Germany) · 2024Review
- WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes.Heliyon · 2024Article
- Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions.Frontiers in molecular biosciences · 2024Article
- Comparison of glycosyl donors: a supramer approach.Beilstein journal of organic chemistry · 2024Article
- Unraveling the promoter effect and the roles of counterion exchange in glycosylation reaction.Science advances · 2023Article
- Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation.Journal of the American Chemical Society · 2023Article
- On the use of real-world datasets for reaction yield prediction.Chemical science · 2023Article
- Predicting Structural Motifs of Glycosaminoglycans using Cryogenic Infrared Spectroscopy and Random Forest.Journal of the American Chemical Society · 2023Article
- HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease.International journal of molecular sciences · 2023Review
- VaporSPOT: Parallel Synthesis of Oligosaccharides on Membranes.Journal of the American Chemical Society · 2022Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and temperature. A random forest algorithm was trained using a highly reproducible, concise dataset to accurately predict the stereoselective outcome of glycosylations. The steric and electronic contributions of all chemical reagents and solvents were quantified by quantum mechanical calculations. The trained model accurately predicts stereoselectivities for unseen nucleophiles, electrophiles, acid catalyst, and solvents across a wide temperature range (overall root mean square error 6.8%). All predictions were validated experimentally on a standardized microreactor platform. The model helped to identify novel ways to control glycosylation stereoselectivity and accurately predicts previously unknown means of stereocontrol. By quantifying the degree of influence of each variable, we begin to gain a better general understanding of the transformation, for example that environmental factors influence the stereoselectivity of glycosylations more than the coupling partners in this area of chemical space.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.