Artificial intelligence for NMR chemical shift prediction
Piroozi G. Thomas B. Kammakakam I.
18 March 2026Cell Press
Cell Reports Physical Science
2026#7Issue 3
Nuclear magnetic resonance (NMR) spectroscopy is a key technique for the determination of molecular structure, but accurate chemical shift prediction remains challenging due to data limitations and the high computational cost of quantum mechanical methods, such as density functional theory (DFT) and gauge-including atomic orbital (GIAO). Recent advances in artificial intelligence (AI) and machine learning (ML) provide fast and scalable alternatives with near DFT accuracy. The field has evolved from descriptor-based models and neural networks (NNs) toward deep learning (DL) approaches, particularly graph neural networks (GNNs) and transformers, which learn molecular representations directly from 2D and three-dimensional (3D) structures. These models have been applied to multiple nuclei and complex molecular systems, with hybrid quantum mechanics (QM)-ML and solvent-aware methods improving accuracy and realism. Despite progress, challenges remain in data quality, generalization to new chemical spaces, and uncertainty quantification. This review summarizes recent methods, datasets, benchmarking practices, and broader AI applications in NMR analysis and spectral processing.
AI , ANNs , chemical shift , GNNs , NMR , transformers
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Department of Chemistry, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 01000, Kazakhstan
Science Division, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi, United Arab Emirates
Department of Chemistry
Science Division
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026