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

通过这一奖项，美国国家科学基金会化学部化学催化计划支持威斯康星大学麦迪逊分校的Daniel Wex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。金属催化的反应是改善对美国S庞大石化资源的管理和发现创新新药的关键。现有的技术是基于稀有金属，如钯和铑，但最近开发出更多富含地球的金属，如镍、铜和铁，显示出巨大的前景。不幸的是，目前几乎没有基于这些更环保、更便宜的金属的催化剂。这种产学合作正在通过开采一种未开发的资源--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，并收集有关哪些特性构成良好催化剂的信息。辉瑞和威斯康星大学麦迪逊分校的研究人员在犹他大学马修·西格曼教授的帮助下，对收集到的数据进行了分析，以指导新催化剂的预测和催化剂的选择。新发现的催化剂正通过与米利波尔-西格玛的合作关系提供给研究人员。这种实验和计算培训的结合正在为学生们促进数据科学在化学中的使用做好准备，这个领域的重要性正在迅速增长。这项培训包括通过与现有和新的UW-Madison项目合作，目前在化学领域代表性不足的学生，这些项目包括化学机会项目、化学研究生院经验合作伙伴和美国化学学会与博士项目的桥梁。UW-Madison团队由威克斯教授领导，Pfizer团队由Hansen博士领导，他们正在系统地在非常大的辉瑞化合物库中搜索新的配体，使用迭代的实验和计算方法，灵感来自基于片段的药物发现。这一合作的目标是发现新的特权配体，并开发出广泛适用的参数和模型。来自化合物文库的各种潜在配体正在根据不同金属和配体要求的已知反应进行筛选，以寻找新的配体核心结构。然后使用传统方法对这些核心结构进行优化。与西格曼教授合作，对收集的数据进行分析，以了解哪些性质(如果有的话)对有用的配体是通用的，并预测改进的配体。这项研究计划的影响延伸到开发新的配体和多功能配体前体，这些配体和前体立即从米利波尔-西格玛公司商业化获得。这项研究还可能产生更好的参数和模型，这些参数和模型对于构建更多样化的配体类型阵列是有用的。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NSF的法定使命，并通过使用基金会的智力优势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.