Directed assembly of pharmaceutical materials: Lessons from intermolecular interactions

这将有助于人工智能（AI）方法的发展，用于预测多组分药物固体的自组装。基于最近在UEA的有机固体结构分析能力的显著增强和药物固体形式分子水平组织的广泛专业知识，该奖学金将使学生能够基于分子药剂学中分子间相互作用的详细知识开发新的预测通用方法。使用先进的实验和计算工具，学生将设计和执行筛选实验，研究溶液和固态中的特定分子间相互作用，并系统地分析生成的固体形式如何响应分子结构的微小变化，以及我们的新模型是否可以正确预测这些响应。

This will contribute to the development of artificial intelligence (AI) methods for predicting self-assembly of multicomponent pharmaceutical solids. Building upon the considerable recent enhancement of capability for structural analysis of organic solids at UEA and extensive expertise in molecular level organisation of pharmaceutical solid forms, this studentship will enable a student to develop novel predictive generic methodologies based on the detailed knowledge of intermolecular interactions in molecular pharmaceutics. Using the advanced experimental and computational tools the student will design and perform screening experiments, investigate specific intermolecular interactions in both solution and solid state and systematically analyse how the generated solid forms respond to small changes in molecular structure and whether our new models can correctly predict those responses.