From NMR to AI: Fusing ¹H and ¹³C Representations for Enhanced QSPR Modeling

The ability to predict log D directly from spectral patterns marks a conceptual shift in cheminformatics. In this work, we demonstrate that 1H and 13C NMR spectra, computationally generated from molecular structures and transformed into machine learning-compatible vectors, can approach and rival classical structure-based descriptors such as ECFP4 fingerprints in modeling the log D parameter. Through comprehensive benchmarking of nearly 70 models across seven algorithmic classes and three pH conditions, we show that concatenation of 1H and 13C NMR spectra offers the best trade-off between accuracy and efficiency. In the best case, a fused spectral CNN model achieved a root-mean-square error (RMSE) of 0.57 and a Q2 of 0.76 using a 400-dimensional input vector─closely matching the ECFP4 benchmark (RMSE 0.56, Q2 0.78) despite being five times smaller. These findings challenge the assumption that descriptor richness must come at the cost of dimensional complexity. SHAP-based analysis revealed modality-specific patterns: 13C regions linked to aromatic and carbonyl carbons (110–170 ppm) increased predicted log D, while 1H signals associated with polar groups, including OH, NH, amides, and ethers (2–4.5 and ∼8 ppm), reduced it. This positions NMR-based vectors as both interpretable and scalable alternatives to conventional fingerprints. By releasing a standalone graphical prediction tool based on our models, we make this paradigm practically accessible for real-world applications. This study establishes in silico-generated NMR spectra as valid and powerful descriptors in predictive modeling, paving the way for spectrum-driven approaches to drug discovery and property prediction.