ArticleJournal of chemical information and modeling2025
Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Chiral atom-centered scalar triple products for predicting enantiomer elution order in HPLC.Journal of computer-aided molecular design · 2026Article
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
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Authors and funding
5 authors.
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Abstract
Absolute stereochemical configurations and optical rotations were computed for 121,416 molecular structures from the QM9 quantum chemistry data set using density functional theory. A representation for the molecules was developed using Cartesian coordinate geometries and encoded atom types to serve as input for various machine learning algorithms. Classifiers were developed and trained to predict the chirality and signs of optical rotations using a variety of machine learning methods. These methods are compared, and the results demonstrate that machine learning is a viable tool for making predictions of the stereochemical properties of molecules.
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Registered trials
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