Next-Generation NMR Crystallography Through Ab Initio Structure Refinement

加州大学滨江分校的Gregory Beran获得了化学系化学理论、模型和计算方法项目的一项奖励，以开发新的计算工具，通过核磁共振（NMR）光谱法促进三维晶体结构的测定。 分子晶体结构的知识在制药和许多其他化学领域是必不可少的。 相同分子的不同晶体堆积基序（“多晶型物”）可以表现出极大不同的性质，并且不期望的多晶型物的出现已经引起了患者和制药商的主要药物召回和其他严重问题。 NMR光谱越来越多地用于确定晶体结构和表征不同的多晶型物。 将NMR光谱中存在的峰组转换为三维晶体结构通常涉及潜在结构的试错计算建模，以识别预测的NMR光谱与实验观察到的最佳匹配的结构。 该项目开发计算工具，以规避这种试错过程，并实现直接的晶体结构细化，可以自动解决产生实验观察到的NMR光谱的晶体结构。 这些新工具将显著提高NMR在解决晶体结构方面的效用。 实现这些目标需要在三个领域取得研究进展。首先，正在开发新的计算实用的电子结构方法，用于模拟控制晶体堆积的非共价相互作用，以识别良好的初始晶体结构。 第二，因为对于给定结构的NMR化学位移的准确预测对于在比较预测和观察到的光谱时区分正确和不正确的结构归属是必不可少的，所以正在开发比广泛使用的现有技术更可靠地预测NMR化学位移的模型。 第三，为了实现直接NMR驱动的晶体结构细化，正在开发机器学习模型，重现分子构象和晶体堆积的变化如何影响化学位移。 成功将导致更大的能力，工程分子晶体所需的性质。 此外，该奖项还支持当地一所小学的外联活动，并制作在线工具，以教育新用户了解计算化学方法。

Gregory Beran of the University of California Riverside is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational tools that will facilitate the determination of three-dimensional crystal structures via nuclear magnetic resonance (NMR) spectroscopy. Knowledge of molecular crystal structures is essential in pharmaceuticals and many other areas of chemistry. Different crystal packing motifs ("polymorphs") of the same molecule can exhibit vastly different properties, and occurrences of undesirable polymorphs have caused major drug recalls and other serious problems for patients and pharmaceutical manufacturers. NMR spectroscopy is increasingly used to determine crystal structures and characterize different polymorphs. Translating the set of peaks present in an NMR spectrum into a three-dimensional crystal structure frequently involves trial-and-error computational modeling of potential structures to identify the structure whose predicted NMR spectrum best matches the experimentally observed one. This project develops computational tools to circumvent this trial-and-error process and enable direct crystal structure refinement which can automatically solve for the crystal structure that produces the experimentally observed NMR spectrum. These new tools will significantly improve the utility of NMR for solving crystal structures. Achieving these goals requires research advances in three areas. First, new computationally practical electronic structure methods for modeling the non-covalent interactions that govern crystal packing are being developed to enable identification of good initial crystal structures. Second, because accurate predictions of the NMR chemical shifts for a given structure are essential to discriminating between correct and incorrect structural assignments when comparing predicted and observed spectra, models which predict NMR chemical shifts more reliably than widely used existing techniques are being developed. Third, to enable direct NMR-driven crystal structure refinement, machine learning models that reproduce how changes in molecular conformation and crystal packing impact the chemical shifts are being developed. Success will lead to greater ability to engineer molecular crystals with desired properties. In addition, this award is supporting outreach activities at a local elementary school and production of online tools to educate new users about computational chemistry approaches.