A Data-Driven Framework for UPLC Method Recommendation in High-Throughput Reaction Analysis

Abstract

High-throughput experimentation (HTE) requires rapid and reliable chromatographic analysis of complex reaction mixtures, yet ultra-performance liquid chromatography (UPLC) method selection remains largely experience-driven and difficult to scale. Here, we present a data-driven framework for reaction-level UPLC method recommendation that elevates retention time (RT) prediction from a molecule-level task to an analytical decision problem defined at the level of complete reaction systems. Method-aware RT prediction models were developed for six predefined gradient methods on a shared reversed-phase UPLC platform. For AM-III, SVR achieved an outer-fold mean R2 of 0.941 ± 0.033 under nested cross-validation; within the chemically relevant similarity regime, median relative errors were below 4.46% for AM-I and 3.44% for AM-II. Predicted RTs of the predefined reaction components were aggregated and evaluated using a reaction-level scoring scheme incorporating retention-window constraints, pairwise separation criteria, component-aware weighting, and a veto rule, enabling systematic ranking of candidate methods prior to experimentation. Application to 500 HTE reaction systems spanning SNAr, Chan−Lam, esterification, Buchwald−Hartwig, and amide coupling reactions identified 422 systems (84.4%) as meeting the predicted-RT screening criteria under at least one candidate UPLC method, with recommendations distributed across multiple chromatographic settings rather than collapsing onto a single default condition. Experimental evaluation of 40 HTE reaction systems showed that 35 recommendations (87.5%) satisfied the predefined observed-RT acceptability criterion, supporting the practical utility of the framework within the tested workflow. This work demonstrates how RT prediction can be operationalized as an effective intermediate representation for scalable chromatographic decision-making in high-throughput chemistry.