Accurate molecular crystal modeling for nuclear magnetic resonance crystallography

加州大学河滨分校的Gregory Beran获得了化学学部化学理论、模型和计算方法（CTMC）项目和计算与数据支持科学与工程项目（CDS&amp；E）的奖励，以开发最先进的计算策略，用于预测最稳定的分子晶体结构，并将这些预测与实验相结合，以确定晶体的包装、药物的有效性。新型有机电子材料和许多其他结晶固体关键取决于分子聚集在一起形成晶体的方式。从长远来看，这些药物晶体包装的研究将有助于提高药物的保质期、溶解度和其他重要性能。这个项目产生的软件将广泛地提供给社区。首席研究员还从事外展活动，为广大学生和公众带来科学计算的意识。为了实现这些目标，正在开发新的电子结构算法，以更低的计算成本提供更准确的分子间相互作用描述，从而实现可靠的晶体结构建模。预测核磁共振化学位移的改进技术正在开发中，以便在实验测量的光谱和三维结构模型之间进行映射。需要高质量的化学位移预测来区分不同的晶体堆积形式。这些技术正被整合到一个结合晶体结构预测、核磁共振谱模拟和实验测量的工作流程中，以确定两种重要药物的几种未知晶体形式的结构。这些新方法被整合到软件中，并将在开源许可条款下发布。该代码将集成到Q-CHEM包中，这是一个拥有非常大用户群的商业代码。

Gregory Beran at the University of California, Riverside is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division and the Computational and Data-Enabled Science and Engineering Program (CDS&E) to develop state-of-the-art computational strategies for predicting the most stable molecular crystal structures and for coupling these predictions with experiments to determine crystal packing, The efficacy of pharmaceuticals, novel organic electronic materials, and many other crystalline solids depends critically on the manner in which the molecules pack together to form crystals. In the longer term, these studies of pharmaceutical crystal packing will help the formulation of medicinal drugs with improved shelf-life, solubility, and other important properties. Software resulting from this project will be made widely available to the community. The principal investigator also engages in outreach activities to bring awareness of scientific computing to the a broad range of students and to the general public.To achieve these goals, new electronic structure algorithms that provide more accurate descriptions of intermolecular interactions at lower computational cost are being developed that will enable reliable crystal structure modeling. Improved techniques for predicting nuclear magnetic resonance chemical shifts are being developed to allow mapping between experimentally measured spectra and three-dimensional structural models. High-quality chemical shift predictions are needed to discriminate among different crystal packing forms. These techniques are being integrated into a workflow that combines crystal structure prediction, nuclear magnetic resonance spectrum simulations, and experimental measurements to identify the structures for several unknown crystal forms of two important pharmaceuticals. The new methods are incorporated into software which will be released to under open-source licensing terms. The code will be integrated into the Q-CHEM package, a commercial code with a very large user base.