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

Abstract

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 195 Pt 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 195 Pt 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 195 Pt 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 195 Pt chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO 2 À , (di)amino, and (di)carboxylate ligands with chemical shift values ranging from À 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.

Layered nanomaterials for renewable energy generation and storage

“…7b). [200][201][202][203] In this section, we will discuss the current state of applications in optimizing various 2D materials and discuss the problems and opportunities in this rapidly developing field.…”

High-throughput Quantum Theory of Atoms in Molecules (QTAIM) for Geometric Deep Learning of Molecular and Reaction Properties

“…hand-selected features based on prior heuristics 3,[16][17][18] and thus, potentially missed leveraging many useful features. With our approach, we integrate a rich set of over 20 atom and 20 bond critical point features for an exhaustive toolkit of electronic descriptors (Table S1).…”

High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties

“…Our goal is to merge the interpretive richness and relevance of QTAIM descriptors with powerful geometric learning algorithms. Previous QTAIM/ML approaches incorporated a limited set of hand-selected features based on existing heuristics, 4,[19][20][21] and thus, potentially missed leveraging many useful features. With our approach, we integrate a rich set of over 20 atom and 20 bond critical point features for an exhaustive toolkit of electronic descriptors (Table S1 †).…”