Predictive computational methods and experimental studies for the discovery of carbohydrate-based catalysts

分子发现通常是一个漫长、低效和昂贵的过程。我们的研究整合了有机，计算和药物化学，以提高合成催化和药物发现的产出率。ACE 1.0（不对称催化剂评价）和FITTED 2.2（通过靶向进化描述诱导的灵活性）已被开发用于分别鉴定不对称催化剂和酶抑制剂。继续这一趋势，该提案提出了改进的分子力学为基础的程序ACE目前资助的NSERC和其应用程序的设计和合成的各种碳水化合物为基础的双功能催化剂。ACE是同类软件中唯一的独立软件，可以快速准确地预测不对称反应的立体选择性。虽然这个程序是准确的两个模型化的反应，改进是必要的，使其更通用和更用户友好的有机化学家，如实施/评估隐式溶剂模型，力场过渡金属参数的发展占各种几何形状的特定金属和建模的玻尔兹曼人口。还提出了其他优化，并将ACE转换为一个虚拟的筛选工具和从头优化工具。我们还将修改ACE以使用ChemDraw文件作为输入。

Molecular discovery is often a long, inefficient and expensive process. Our research integrates organic, computational, and medicinal chemistry in order to increase the output rate in both synthetic catalysis and drug discovery. ACE 1.0 (Asymmetric Catalyst Evaluation) and FITTED 2.2 (Flexibility Induced Through Targeted Evolutionary Description) have been developed to identify asymmetric catalysts and enzyme inhibitors respectively. Continuing this trend this proposal presents improvements of the molecular mechanics-based program ACE currently funded by NSERC and its application to the design and synthesis of a variety of carbohydrate-based bifunctional catalysts. ACE, which is the only independent software of its kind, quickly and accurately predicts the stereoselectivity of asymmetric reactions. Although this program is accurate on two modeled reactions, improvements are necessary to make it more versatile and more user-friendly for organic chemists such as implementation/evaluation of implicit solvent models, development of force field transition metal parameters accounting for various geometries of specific metals and modeling of the Boltzmann population. Other optimizations are also proposed and will convert ACE into a virtual screening tool and a de novo optimization tool. We will also modify ACE to use ChemDraw files as input.