ArticleAnalytical chemistry2026
Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation-Mobile Phase (EES-MP) Profiles for MP Selection─A Lux Cellulose-1 Case Study.
Article in Analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
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5 authors.
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Abstract
Chiral HPLC method development still relies heavily on trial-and-error screening. We introduce the Efficient Enantioseparation (EES) parameter─a single metric integrating resolution (Rs) and retention (k)─to move from point predictions to full mobile-phase (MP) profile modeling. Using EES as the response, we trained multiple artificial neural networks (ANNs) on 62 variables (molecular descriptors) and 76 objects (structurally diverse neutral and basic compounds chromatographed on a Lux Cellulose-1 column under aqueous-acetonitrile conditions at nine MP compositions). ANNs were optimized with a chaotic competitive-learning optimizer (CCLNNA), then ranked/selected and combined into a consensus model to enhance robustness and limit overfitting. The ANN-consensus model accurately reproduces full EES-MP profiles (
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