Uncertainty-Aware Prediction of ¹⁹⁵Pt Chemical Shifts from Limited Data

Platinum (Pt) complexes are highly relevant for medicinal chemistry and homogeneous catalysis. In the development of novel Pt-based chemotherapeutic agents and catalysts, characterization of the compounds using nuclear magnetic resonance (NMR) spectroscopy of the ¹⁹⁵Pt nucleus is standard. However, measuring ¹⁹⁵Pt-NMR signals can be tedious due to the large chemical shift range and limited resolution. To facilitate experimental measurements by narrowing down the shift range, reliable predictions of the chemical shift are needed. Especially for lighter nuclei such as ¹H and ¹³C, machine learning (ML) methods predict chemical shifts accurately, while analogous models for heavier nuclei are scarce. In this work, we propose Gaussian Process Regression (GPR) models for the prediction of ¹⁹⁵Pt chemical shifts. The underlying data set comprises 292 structures and three different descriptors were used to encode structural and chemical features of the molecules. Based on the prediction uncertainties derived from the posterior variance of the models, a reasonably narrow shift range can be estimated for a given Pt complex. The most robust model yields a mean absolute error (MAE) of 114 ppm on the holdout test set, which is significantly more accurate than relativistic DFT calculations.