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

ABSTRACT: 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 o

SUBMITTER: Leniak A 

PROVIDER: S-EPMC12529761 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature