Physics-Informed Neural Networks for Automated Extraction of Scalar Coupling Constants and Chemical Shifts from Strongly Coupled NMR Spectra: ABC and ABCD Spin Systems

We present an automated pipeline for extracting scalar coupling constants J and chemical shifts from strongly coupled 1H NMR spectra of ABC and ABCD spin systems. It operates on an isolated peak list of the target spin system without requiring initial parameters or manual transition assignment. It combines a neural-network (NN) stage with a quantum-mechanical refinement. The two spin-system classes are handled by two architectures tailored to their respective inverse problems: an ABC network built on a Set Transformer backbone and an ABCD network designed to be topology-agnostic through a coupling-conditioned shift-prediction scheme. On synthetic and experimentally acquired 1H spectra at 60, 80 and 500 MHz, the ABC pipeline achieves mean sub-0.1 Hz accuracy on both J and chemical shifts across the experimental benchmark. The ABCD network, trained on a topology-diverse synthetic data set spanning four structurally distinct molecular families, reaches MAE(J) ≈ 0.13 Hz and MAE(ν) ≈ 0.33 Hz on a held-out synthetic evaluation subset comprising the validation and test partitions. On an ABCD validation set comprising experimental and DFT-based spectra, the full pipeline further achieves MAE(J) ≈ 0.08 Hz and MAE(ν) ≈ 0.30 Hz without requiring topology information. These accuracies are below, or well within, typical 1H linewidths at all three field strengths considered, and the entire pipeline runs in tens of milliseconds per spectrum, combining sub-linewidth precision with throughput suitable for routine automated processing of large experimental datasets.