Assigning the stereochemistry of natural products by machine learning.
Publisher DOI
PubMed ID
42035200
Abstract
Nature has settled for L-chirality for proteinogenic amino acids and D-chirality for the carbohydrate backbone of nucleotides. Further stereochemical patterns exist among natural products produced by common biosynthetic pathways. Here we asked the question whether these regularities might be sufficiently prevalent among natural products (NPs) such that their stereochemistry could be machine learned and assigned automatically. Indeed, we report that a language model can be trained to assign the stereochemistry of NPs using the open access NP database COCONUT. In detail, our language model, called NPstereo, translates an NP structure written as absolute SMILES into the corresponding isomeric SMILES notation containing stereochemical information, with 80.2% per-stereocenter accuracy for full assignments and 85.9% per-stereocenter accuracy for partial assignments, across various NP classes including secondary metabolites such as alkaloids, polyketides, lipids and terpenes. NPstereo might be useful to assign or correct the stereochemistry of newly discovered NPs.
Date Issued
2026-04-25
Publication Type
Article
Subjects
Chirality
•
Language models
•
Machine learning
•
Natural products
•
Stereochemistry
Language(s)
en
Journal
Journal of Cheminformatics
Publisher
BioMed Central
ISSN
1758-2946
Access(Rights)
open.access