Computational Models for New Developments in Radical Chemistry

自由基化学新发展的计算模型

基本信息

批准号：
10720509
负责人：
Robert Scott Paton
金额：
$ 33.59万
依托单位：
COLORADO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720509
关键词：
Acceleration Acids Address Alkenes Catalysis Chemistry Collaborations Complex Computer Models Coupled Cyclization Cytochrome P450 Degradation Pathway Development Electronics Energy Transfer Generations Goals Halogens Hydrogen Kinetics Machine Learning Mediating Medicine Metabolic Metabolism Methods Modeling Modernization Molecular Pharmaceutical Chemistry Preparation Process Protocols documentation Rationalization Reaction Reagent Research Role Site Structure Synthesis Chemistry Thermodynamics Triplet Multiple Birth catalyst chlorination computational platform computerized tools cycloaddition design drug candidate drug-like compound guided inquiry halogenation insight intermolecular interaction machine learning model novel therapeutics oxidation predictive modeling predictive tools programs reaction rate screening small molecule tool triplet state

项目摘要

Project Abstract The goal of this project is to develop new understanding and predictive models for the formation, reactivity, and selectivity of organic radical and diradical intermediates. Triplet diradicals can undergo a variety of transformations that are not accessible in the singlet ground state. However, developing efficient photocatalytic triplet energy transfer processes, particularly in an enantioselective fashion, remains an enduring goal. We will show that computational approaches can be leveraged to develop general principles for substrate and sensitizer design to harness triplet-state reactivity. We will use computation to target the mechanism-guided discovery of unexplored reactivity in the triplet state, such as homolytic aromatic substitution, and the design of chiral Lewis acids to promote asymmetric photocatalytic cyclizations. We will also develop a qualitative and quantitative understanding of the factors controlling the reaction rates and site selectivities of radical homolytic substitution, such as hydrogen and halogen atom transfer reactions. These conceptual insights will be used to rationalize experimental observations and underpin the development of new radical reagents for site-selective C(sp3)-H chlorination. The development of quantitative models, aided by new physical-organic parameters, can be used to accelerate this process. Machine learning models grounded in mechanistic understanding will provide new tools to parametrize substrates and reagents to accelerate reaction discovery and optimization. We will employ this strategy to predict the site-selectivity of P450 oxidation small molecules and to establish general workflows to predict the metabolic degradation pathways.

项目摘要该项目的目的是为形成，反应性和有机自由基和脏自动中间体的选择性。三胞胎Diradicals会经历各种各样的在单线基态无法访问的转换。但是，发展有效的光催化 Triplet Energion Transws流程，尤其是以对映选择性方式，仍然是一个持久的目标。我们将表明可以利用计算方法来开发底物和敏化器的一般原则设计到线束三重态反应性。我们将使用计算来定位机制引导的发现三胞胎状态中未开发的反应性，例如均利性芳香替代品和手性刘易斯的设计酸促进不对称光催化环化。我们还将开发一个定性和定量的了解控制反应速率和部位选择性的因素，从而例如氢和卤素原子转移反应。这些概念见解将用于合理化实验性观察并支撑了现场选择性C（SP3）-H的新自由基试剂的开发氯化。可以使用新的物理有机参数的帮助的定量模型的开发加速这一过程。基于机械理解的机器学习模型将提供新的参数化基材和试剂以加速反应发现和优化的工具。我们将雇用这种预测P450氧化小分子并建立一般工作流程的位点选择性的策略预测代谢降解途径。