Predicting 195Pt NMR Chemical Shifts in Water-Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Water-soluble Pt complexes are the key components in medicinal chemistry and catalysis. The well-known cisplatin family of anticancer drugs and industrial hydrosylilation catalysts are two leading examples. On the molecular level, the activity mechanisms of such complexes mostly involve changes in the Pt coordination sphere. Using Pt-195 NMR spectroscopy for operando monitoring would be a valuable tool for uncovering the activity mechanisms; however, reliable approaches for the rapid correlation of Pt complex structure with Pt-195 chemical shifts are very challenging and not available for everyday research practice. While NMR shielding is a response property, molecular 3D structure determines NMR spectra, as widely known, which allows us to build up 3D structure to Pt-195 chemical shift correlations. Accordingly, we present a new workflow for the determination of lowest-energy configurational/conformational isomers based on the GFN2-xTB semiempirical method and prediction of corresponding chemical shifts with a Machine Learning (ML) model tuned for Pt complexes. The workflow was designed for the prediction of Pt-195 chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO2-, (di)amino, and (di)carboxylate ligands with chemical shift values ranging from -6293 to 7090 ppm. The model offered an accuracy (normalized root-mean-square deviation/RMSD) of 1.08 %/145.02 ppm on the held-out test set.

Automated summary

Water-soluble Pt complexes are critical in medicinal chemistry and catalysis, with cisplatin family drugs and hydrosylilation catalysts being notable examples. Understanding the activity mechanisms of these complexes requires reliable approaches for correlating structure with Pt-195 chemical shifts, which is currently challenging. In this study, a new workflow combining semiempirical calculations and machine learning was developed to predict Pt-195 chemical shifts of water-soluble Pt(II) and Pt(IV) complexes.