Active Site Identification in FeNC Catalysts and Their Assignment to the Oxygen Reduction Reaction Pathway by In Situ 57

FeNC catalysts are the most promising substitutes for Pt‐based catalysts for the oxygen reduction reaction in proton exchange fuel cells. However, it remains unclear which FeN4 moieties contribute to the reaction mechanism and in which way. The origin of this debate could lie in various preparation routes, and therefore the aim of this work is to identify whether the active site species differ in different preparation routes or not. To answer this question, three FeNC catalysts, related to the three main preparation routes, are prepared and thoroughly characterized. Three transitions A–C that are distinguished by a variation in the local environment of the deoxygenated state are defined. By in situ 57Fe Mössbauer spectroscopy, it can be shown that all three catalysts exhibit a common spectral change assigned to one of the transitions that constitutes the dominant contribution to the direct electroreduction of oxygen. Moreover, the change in selectivity can be attributed to the presence of a variation within additional species. Density functional theory calculations help to explain the observed trends and enable concrete suggestions on the nature of nitrogen coordination in the two FeN4 moieties involved in the oxygen reduction reaction of FeNC catalysts

Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4

Inhibition of cancer-promoting kinases is an established therapeutic strategy for the treatment of many cancers, although resistance to kinase inhibitors is common. One way to overcome resistance is to target orthogonal cancer-promoting pathways. Bromo and Extra-Terminal (BET) domain proteins, which belong to the family of epigenetic readers, have recently emerged as promising therapeutic targets in multiple cancers. The development of multitarget drugs that inhibit kinase and BET proteins therefore may be a promising strategy to overcome tumor resistance and prolong therapeutic efficacy in the clinic. We developed a general computational screening approach to identify novel dual kinase/bromodomain inhibitors from millions of commercially available small molecules. Our method integrated machine learning using big datasets of kinase inhibitors and structure-based drug design. Here we describe the computational methodology, including validation and characterization of our models and their application and integration into a scalable virtual screening pipeline. We screened over 6 million commercially available compounds and selected 24 for testing in BRD4 and EGFR biochemical assays. We identified several novel BRD4 inhibitors, among them a first in class dual EGFR-BRD4 inhibitor. Our studies suggest that this computational screening approach may be broadly applicable for identifying dual kinase/BET inhibitors with potential for treating various cancers