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

Here, a reference-guided large language model (large language model) workflow for thin-layer chromatography mobile phase recommendation is proposed.

The large language model is used as a flexible inference interface that integrates structural similarity, polarity descriptors, and example-based analogical inference.

Using a publicly available high-throughput automated thin-layer chromatography 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.