A reference-guided large language model workflow for mobile phase selection in thin-layer chromatography enabled by a...

Mobile phase selection in thin-layer chromatography (TLC) still relies heavily on empirical trial-and-error. Existing machine learning methods often exhibit limited predictive performance, as they depend on manual descriptor engineering and are sensitive to data quality. Here, a reference-guided large language model (LLM) workflow for TLC mobile phase recommendation is proposed. The LLM is used as a flexible inference interface that integrates structural similarity, polarity descriptors, and example-based analogical inference. The key methodological contribution is a polarity-space tetrahedron strategy for selecting reference compounds. Three polarity descriptors, including molecular refractivity, topological polar surface area, and n-octanol-water partition coefficient, are used to construct a three-dimensional space, and a target compound is constrained within a tetrahedron formed by four reference compounds to enable interpolation-based inference. A similarity-weighted version of this tetrahedron strategy is also developed. Using a publicly available high-throughput automated TLC dataset and DeepSeek-Reasoner as the inference engine, four reference selection strategies are compared. The similarity-weighted tetrahedron method achieves the best performance, with an availability of 81.48% and a final score of 0.8436 across 45 compound pairs evaluated in triplicate. The success rate of 92.16% confirmed by independent experimental validation supports the practical relevance of the recommendations. The framework also provides interpretable outputs, including structured rationales and experimental suggestions. This work demonstrates that combining a locally constrained reference construction strategy with an LLM offers a practical and interpretable tool for TLC mobile phase optimization, without requiring large labeled datasets or task-specific model training.