Computational Explorations of Unconventional Approaches to Control Noncovalent Interactions

控制非共价相互作用的非常规方法的计算探索

• 批准号: 10016376
• 负责人: Judy I-Chia Wu
• 金额: $ 37.72万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016376
• 关键词: Anions; Back; Biosensor; Chemicals; Chemistry; Electrons; Experimental Designs; Goals; Hydrogels; Hydrogen Bonding; Intuition; Investigation; Modeling; Molecular; Outcome; Plant Roots; Research; Research Project Summaries; Research Proposals; System; Vision; Work; cheminformatics; density; improved; innovation; interest; machine learning method; novel; quantum computing; receptor; theories; tool

PROJECT SUMMARY The research interests of my group are rooted in explorations of new and useful conceptual models to improve the control and prediction of noncovalent interactions. Our research involves the use of a variety of computational quantum chemical tools, applications of density functional theory (DFT), cheminformatics, and machine-learning methods. A premise of our research is that aromaticity may be used to modulate many types of noncovalent interactions (such as hydrogen bonding, π-stacking, anion-π interactions). The reciprocal relationship we find, between “aromaticity” in molecules and the strengths of “noncovalent interactions,” is surprising especially since they are typically considered as largely separate ideas in chemistry. The innovation of this research is that it will enable use of intuitive “back-of-the-envelope” electron-counting rules (such as the 4n+2πe Hückel rule for aromaticity) to make predictions of experimental outcomes regarding the impact of noncovalent interactions. A five-year goal is to realize the use of our conceptual models in real synthetic examples prepared by our experimental collaborators. My research vision is to bridge discoveries of innovative concepts to their practical impacts for biomedical and biomolecular research.

项目摘要 我们小组的研究兴趣植根于探索新的和有用的概念模型，以改善 控制和预测非共价相互作用。我们的研究涉及使用各种 计算量子化学工具，密度泛函理论（DFT）的应用，化学信息学， 机器学习方法我们研究的一个前提是芳香性可以用来调节许多类型 非共价相互作用（如氢键，π堆积，阴离子-π相互作用）。倒数 我们发现，分子中的“芳香性”和“非共价相互作用”的强度之间的关系是 令人惊讶的是，特别是因为它们通常被认为是化学中很大程度上独立的概念。创新 这项研究的一个重要特点是，它将使人们能够使用直观的“信封背面”电子计数规则（如 4 n +2πe Hückel芳香性规则）来预测实验结果， 非共价相互作用五年的目标是实现我们的概念模型在真实的合成 由我们的实验合作者准备的例子。我的研究愿景是将创新的发现 这些概念对生物医学和生物分子研究的实际影响。