Make Informed Decisions From Experimental Design To Data Interpretation

Accurate NMR prediction underpins confident structure verification, clean spectral assignment, and faster experimental decisions, especially when working with complex natural products. Real-world performance matters more than theoretical promise, and practical accuracy can vary widely across prediction platforms. By benchmarking thousands of experimentally validated ¹³C chemical shifts against diverse, challenging molecular structures, clear differences emerge in how modern tools handle complexity. Predictive approaches, including neural networks and HOSE-based methods, shape not only accuracy but also consistency and reliability when spectra are crowded or ambiguous. For chemists supporting discovery, synthesis, and analytical workflows, understanding these differences helps set realistic expectations and identify where stronger prediction reduces uncertainty, rework, and time spent resolving spectra.

Explore the full infographic to see side-by-side comparisons and pinpoint what separates dependable predictions from the rest.