Guest Jamie L, Bourne Esther A E, Screen Martin A, Wilson Mark R, Pham Tran N, Hodgkinson Paul
Department of Chemistry, Durham University, Lower Mountjoy, Stockton Road, Durham, UK.
GSK, Gunnels Wood Road, Stevenage, UK.
Faraday Discuss. 2025 Jan 8;255(0):325-341. doi: 10.1039/d4fd00097h.
Molecular dynamics (MD) simulations and chemical shifts from machine learning are used to predict N, C and H chemical shifts for the amorphous form of the drug irbesartan. The local environments are observed to be highly dynamic well below the glass transition, and averaging over the dynamics is essential to understanding the observed NMR shifts. Predicted linewidths are about 2 ppm narrower than observed experimentally, which is hypothesised to largely result from susceptibility effects. Previously observed differences in the C shifts associated with the two tetrazole tautomers can be rationalised in terms of differing conformational dynamics associated with the presence of an intramolecular interaction in one tautomer. H shifts associated with hydrogen bonding can also be rationalised in terms of differing average frequencies of transient hydrogen bonding interactions.
分子动力学(MD)模拟和机器学习化学位移被用于预测药物厄贝沙坦无定形形式的氮、碳和氢化学位移。观察到局部环境在远低于玻璃化转变温度时具有高度动态性,对动力学进行平均对于理解观察到的核磁共振位移至关重要。预测的线宽比实验观察值窄约2 ppm,据推测这主要是由磁化率效应导致的。先前观察到的与两种四氮唑互变异构体相关的碳位移差异,可以根据一种互变异构体中存在分子内相互作用所伴随的不同构象动力学来解释。与氢键相关的氢位移也可以根据瞬态氢键相互作用的不同平均频率来解释。