GOALI: D3SC: New Ligands and Understanding from Pharmaceutical Compound Libraries

美国国家科学基金会化学部的化学催化项目通过这一奖项来支持威斯康星大学麦迪逊分校的Daniel Weix教授和辉瑞公司的Eric Hansen博士的研究，以探索寻找更好的化学反应催化剂的新方法。金属催化反应是改善美国管理的关键美国丰富的石油化工资源，以及发现创新的新药。现有的技术是基于稀有金属，如钯和铑，但最近对地球上更丰富的金属，如镍、铜和铁的发展显示出很大的希望。不幸的是，目前很少有基于这些更环保、更便宜的金属的催化剂。这种工业-学术合作伙伴关系正在通过挖掘尚未开发的资源-药物化合物库来发现新的催化剂。利用这些知识库，该团队正在挖掘数据，以寻找新的催化剂，并收集有关什么性质可以成为好的催化剂的信息。在犹他大学马修·西格曼教授的协助下，辉瑞公司和威斯康星大学麦迪逊分校的研究人员对收集到的数据进行了分析，指导了对新催化剂和催化剂选择的预测。新发现的催化剂正在通过与Millipore-Sigma的合作伙伴关系提供给研究人员。这种实验和计算训练的结合使学生们准备好在化学领域推进数据科学的应用，这一领域的重要性正在迅速增长。通过与现有和新的威斯康星大学麦迪逊分校项目的合作伙伴关系，该培训包括目前在化学领域代表性不足的学生：化学机会项目、化学研究生院经验合作伙伴和美国化学学会通往博士学位项目的桥梁。威斯康星大学麦迪逊分校的研究小组由Weix教授领导，辉瑞公司的研究小组由Hansen博士领导，他们利用基于片段的药物发现启发的迭代实验和计算方法，系统地搜索辉瑞公司非常大的化合物库，寻找新的配体。此次合作的目标是发现新的特权配体，并开发广泛适用的参数和模型。从化合物文库中筛选各种潜在的配体，以针对已知的不同金属和配体需求的反应进行筛选，以寻找新的配体核心结构。然后使用常规方法对这些核心结构进行优化。与Sigman教授合作，对收集到的数据进行分析，以了解哪些属性（如果有的话）对于有用的配体是普遍的，并预测改进的配体。这项研究计划的影响延伸到新配体和多功能配体前体的开发，这些配体和多功能配体前体立即从Millipore-Sigma商业化。这项研究还可能产生更好的参数和模型，用于构建更多样化的配体类型阵列。大型数据集有助于开发通过数据存储库提供的新计算方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With this award, the Chemical Catalysis Program of the NSF Division of Chemistry is supporting the research of Professor Daniel Weix at the University of Wisconsin-Madison and Dr. Eric Hansen at Pfizer to explore new methods to find better catalysts for chemical reactions. Metal-catalyzed reactions are the key to improving the stewardship of the U.S.'s vast petrochemical resources and to discovering innovative, new medicines. Existing technology is based around scarce metals, such as palladium and rhodium, but recent developments with more earth-abundant metals, such as nickel, copper, and iron, show great promise. Unfortunately, there are currently few catalysts based around these more environmentally friendly and less expensive metals. This industrial-academic partnership is discovering new catalysts by mining an untapped resource, pharmaceutical compound libraries. Using these libraries of knowledge, the team is mining data to find new catalysts and to gather information on what properties make a good catalyst. Analysis of the collected data by researchers at Pfizer and UW-Madison, with the assistance of Professor Matthew Sigman at the University of Utah, guides the prediction of new catalysts and catalyst selection. The newly discovered catalysts are being made available to researchers through a partnership with Millipore-Sigma. This combination of experimental and computational training is preparing students to advance the use of data science in chemistry, an area that is rapidly growing in importance. This training includes students who are currently underrepresented in chemistry through partnerships with existing and new UW-Madison programs: the Chemistry Opportunities Program, Partners for Graduate School Experience in Chemistry, and the American Chemical Society BRIDGE to the Doctorate program.The UW-Madison team, led by Professor Weix, and the Pfizer team, led by Dr. Hansen, are systematically searching the very large Pfizer compound library for new ligands using an iterative experimental and computational approach inspired by fragment-based drug discovery. The goals of this collaboration are to discover new privileged ligands and to develop broadly applicable parameters and models. Diverse potential ligands sourced from the compound library are being screened against known reactions with different metal and ligand requirements to find new ligand core structures. These core structures are then being optimized using conventional methods. The data gathered is analyzed, in collaboration with Professor Sigman, to provide an understanding of which properties (if any) are universal for useful ligands and to predict improved ligands. The impacts of this this research program extend to the development of new ligands and versatile ligand precursors that are immediately made commercially available from Millipore-Sigma. The research may also result in better parameters and models that are useful for constructing a more diverse array of ligand types. The large data sets are helpful for developing new computational approaches made available through data repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.