Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.

Liquid chromatography is a predominant technology for the separation of small molecules. Hundreds of retention time prediction models have been published over the past decades, yet truly transferable prediction (requiring no training data from the target chromatographic system) remains an open challenge. Unfortunately, retention times may change massively, even for nominally identical chromatographic conditions. Retention order is considerably more conserved; but even retention order may change if chromatographic conditions vary. Here we present 2-step, a two-step method for the prediction of retention times in reversed-phase chromatography. In the first step, a machine-learning model predicts a retention order index, taking into account chromatographic conditions. In the second step, we map predicted indices to absolute retention times. Disentangling these two tasks finally enables transferable retention time prediction across chromatographic conditions and compound classes, without requiring any target-system training data. Our 2-step method outperforms existing methods that were trained on the target dataset. Finally, we systematically study what chromatographic conditions result in notable changes of retention order.