Consensus descriptor recurrence across complementary ANN workflows in chiral HPLC: An interaction-domain analysis.

Abstract

Interpreting descriptor relevance in chiral high-performance liquid chromatography (HPLC) remains challenging because enantioseparation depends on multiple correlated structural and chromatographic factors. Here, descriptor-selection recurrence was compared across two consensus artificial neural network (ANN) workflows applied to the same Lux Cellulose-1/aqueous-acetonitrile system. The analysis used 76 learning-stage compounds for model optimization and descriptor-frequency analysis, with two external-test compounds reserved for a limited independent decision-level check. The workflows comprised a previously reported efficient enantioseparation (EES)-mobile-phase profile predictor and a new decision-oriented model for optimal mobile-phase recommendation. To reduce overinterpretation at the single-descriptor level, recurrent selection patterns were examined at both descriptor and interaction-domain levels, acknowledging that multiple descriptors may encode overlapping physicochemical information. Frequency-based analysis revealed distinct interaction-domain trends: the decision-oriented workflow was dominated by hydrogen-bond-related descriptors, whereas EES-profile prediction across the nine tested mobile phases favored topological/structural descriptors. π-π descriptors remained recurrent contributors in both workflows, with greater quantitative prominence in the predictive workflow. These results indicate that descriptor recurrence can serve as an operational proxy to explore and compare interaction-domain trends within the present dataset, but not as direct mechanistic proof. Beyond prediction, the EES-driven ANN framework enabled the extraction of chemically interpretable interaction-domain fingerprints, offering practical guidance for chiral stationary phase/mobile phase selection and cautious data-driven rationalization of chiral recognition.