Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation–Mobile Phase (EES–MP) Profiles for MP SelectionA Lux Cellulose-1 Case Study

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 (R2 > 0.9) with lower error dispersion, enabling prospective feasibility checks and single-shot selection of high-EES mobile-phase compositions. External tests on fluoxetine and lormetazepam confirmed prospective utility by anticipating separability at one or more MPs (nominating the MP with maximal EES) or nonseparability across the explored MP range. To our knowledge, this work provides the first proof-of-concept for in silico prediction of full EES–MP profiles in chiral HPLC, enabling intelligent MP selection. Rather than a definitive model, this work evaluates the potential of the strategy: consensus stabilizes learning with limited data and offers greener, actionable guidance that can reduce experiments, reagent consumption, and development time. The framework is extensible to broader chemotypes and stationary and mobile phases; larger data sets could further generalize EES-profile prediction and support intelligent MP selection in sustainable chiral HPLC.