Physical Implausibility of Carbohydrate Ligands in Results of Deep Learning-Based Cofolding Methods

Stereochemistry violations in AlphaFold 3 models are more prevalent than currently appreciated. Analysis of 900 carbohydrate ligands revealed that 85.8% have errors, mainly in chirality but also including bond conversions (15.2%), planar ring distortions (3.9%), aromatic ring formations (2.5%), and improper structural configurations (0.9%). Boltz-1x reduced most violations dramatically but increased improper configurations to 22.1%, notably in <i>N</i>-acetyl-α-neuraminic acid. The BondedAtomPairs protocol reduced stereochemical issues but lost the reducing-end anomeric oxygen, highlighting ongoing challenges in accurate carbohydrate modeling.

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Journal
Journal of Chemical Information and Modeling
Published
2026-03-22
DOI
https://doi.org/10.1021/acs.jcim.5c03075
Primary Topic
Molecular spectroscopy and chirality
Type
article
Field-Weighted Citation Impact
0.00
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article

Physical Implausibility of Carbohydrate Ligands in Results of Deep Learning-Based Cofolding Methods

Daniel J. Rigden, Luc Elliott, Adam J. Simpkin, Muhammad Luthfi et al.
Journal of Chemical Information and Modeling
Molecular spectroscopy and chirality
article

Physical Implausibility of Carbohydrate Ligands in Results of Deep Learning-Based Cofolding Methods

Daniel J. Rigden, Luc Elliott, Adam J. Simpkin, Muhammad Luthfi, Pornthep Sompornpisut
article en

Abstract

Stereochemistry violations in AlphaFold 3 models are more prevalent than currently appreciated. Analysis of 900 carbohydrate ligands revealed that 85.8% have errors, mainly in chirality but also including bond conversions (15.2%), planar ring distortions (3.9%), aromatic ring formations (2.5%), and improper structural configurations (0.9%). Boltz-1x reduced most violations dramatically but increased improper configurations to 22.1%, notably in <i>N</i>-acetyl-α-neuraminic acid. The BondedAtomPairs protocol reduced stereochemical issues but lost the reducing-end anomeric oxygen, highlighting ongoing challenges in accurate carbohydrate modeling.

Journal of Chemical Information and Modeling
University of Liverpool (GB), Chulalongkorn University (TH), Laboratoire d’immunologie intégrative du cancer (FR), Computational Physics (United States) (US)
Openalex Percentile: Top 12%
Molecular spectroscopy and chirality
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Physical Implausibility of Carbohydrate Ligands in Results of Deep Learning-Based Cofolding Methods — Daniel J. Rigden, Luc Elliott, et al. · Journal of Chemical Information and Modeling (2026) | TGRS Research Map | TGRS