Times are changing but order matters: Transferable prediction of small molecule liquid chromatography retention times

Hundreds of models for the prediction of small molecule retention times were published during the last decades. Our goal is the transferable prediction of retention times: Our method should predict retention times for a target dataset, without the need of training data from that chromatographic system. Unfortunately, retention times may change massively, even for nominally identical chromatographic conditions. Retention order is much better retained, yet even the retention order of compounds may change if chromatographic conditions vary. We present a machine learning model that can predict retention order or, more precisely, a retention order index, taking into account chromatographic conditions. We show how to map predicted retention order indices to retention times. Disentangling these two task finally enables transferable retention time prediction across chromatographic conditions and compound classes. 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.