Practical Machine Learning Strategies. I. Correcting the MMFF Molecular Mechanics Model to More Accurately Provide Conformational Energy Differences in Flexible Organic Molecules.

A correction to the MMFF molecular mechanics model, based on a neural network trained to reproduce conformer energy differences obtained from ωB97X-V/6-311+G(2df,2p)[6-311G*]//MMFF calculations is described. It is supported for molecules containing H, C, N, O, F, S, Cl, and Br. The correction adds only slightly to the cost of MMFF, and the resulting corrected model is several orders of magnitude faster than ωB97X-V/6-311+G(2df,2p)[6-311G*].

Efficient Protocol for Accurately Calculating C Chemical Shifts of Conformationally Flexible Natural Products: Scope, Assessment, and Limitations.

An efficient protocol for calculating C NMR chemical shifts for natural products with multiple degrees of conformational freedom is described. This involves a multistep procedure starting from molecular mechanics and ending with a large basis set density functional model to obtain accurate Boltzmann conformer weights, followed by empirically corrected density functional NMR calculations for the individual conformers. The accuracy of the protocol (average rms <4 ppm) was determined by application to ∼925 diverse natural products, the structures of which have been confirmed either by X-ray crystallography or independent synthesis.