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标是开发新的理解和预测模型来了解形成、反应性和有机自由基和双自由基中间体的选择性。三重态双自由基可以经历多种在单重态基态下无法进行的变换。然而，开发高效的光催化三线态能量转移过程，特别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剂的一般原理设计利用三重态反应性。我们将使用计算来实现机制引导的发现三重态中未探索的反应性，例如均裂芳族取代，以及手性路易斯的设计酸促进不对称光催化环化。我们还将制定定性和定量的了解控制自由基均裂取代反应速率和位点选择性的因素，例如氢和卤素原子的转移反应。这些概念见解将用于合理化实验观察并支持用于位点选择性 C(sp3)-H 的新型自由基试剂的开发氯化。在新的物理有机参数的帮助下，可以使用定量模型的开发来加速这个过程。基于机械理解的机器学习模型将提供新的用于参数化底物和试剂以加速反应发现和优化的工具。我们将聘用该策略可预测 P450 氧化小分子的位点选择性并建立一般工作流程预测代谢降解途径。