The concept utilized is known as ensemble learning, which allows multiple machine-learning algorithms to be combined.

These predicted chemical shifts will be combined using a Bayesian method to produce the final chemical shift, which has higher reliability and accuracy than the individual chemical shifts. When an NMR prediction is made, each of these predictors will predict chemical shifts with a certain confidence interval based on different methods, for example with HOSE, along with substituent chemical shifts approach for Modgraph predictor, and CHARGE for MestreLab Predictor. MestReNova prediction is based on two predictors: MestreLab Predictor and Modgraph Predictor . NMRShiftDB that only uses NNs in predicting NMR shifts. Another predictor’s ability was compared, i.e. These predictor’s ability was compared with ChemDraw, a predictor that only uses a single prediction method: linear additivity rules. MestReNova and ACD Workbook suite were selected based on the similarity of what they both possess i.e., having the conventional hierarchically ordered spherical description of environment (HOSE) as one of their predictors but the latter having neural network (NN) as an additional tool that can interpolate within its trained database. The focus of this study is to evaluate the accuracy of 1H NMR predictions’ software using statistical tools namely on MestReNova, ACD Workbook Suite, ChemDraw and NMRShiftDB. NMR predictions’ software primarily rely on coding and computational methods in producing NMR predictions, different programs can use different methods in their internal calculations for predictions.

Accurate predictions of NMR chemical shifts with respect to experimental values are highly valuable for structural elucidations. The molecular environment around a nucleus determines its chemical shift, and its interpretation is important to elucidate the structure of organic molecules. Molecular structure elucidation is the determination of chemical structures which can be achieved by using NMR spectroscopy techniques.

Mestrenova chemdraw