ACS Combinatorial Science最新文献

High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic materials thin film libraries, in which various films prepared under different process conditions are integrated on a single substrate. Here, we demonstrate that high-resolution grazing incident XRD mapping analysis is useful for this purpose: A 2-dimensional organic combinatorial thin film library with the composition and growth temperature varied along the two orthogonal axes was successfully analyzed by using synchrotron microbeam X-ray. Moreover, we show that the time-consuming mapping process is accelerated with the aid of a machine learning technique termed as Bayesian optimization based on Gaussian process regression.

DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against a protein of interest, whereby compounds retained by the target are subsequently identified by next-generation sequencing of the corresponding DNA tags. When analyzing the resulting data, one typically assumes that sequencing output (i.e., read counts) is proportional to the binding affinity of a given compound, thus enabling hit prioritization and elucidation of any underlying structure–activity relationships (SAR). This assumption, though, tends to be severely confounded by a number of factors, including variable reaction yields, presence of incomplete products masquerading as their intended counterparts, and sequencing noise. In practice, these confounders are often ignored, potentially contributing to low hit validation rates, and universally leading to loss of valuable information. To address this issue, we have developed a method for comprehensively denoising DEL selection outputs. Our method, dubbed “deldenoiser”, is based on sparse learning and leverages inputs that are commonly available within a DEL generation and screening workflow. Using simulated and publicly available DEL affinity selection data, we show that “deldenoiser” is not only able to recover and rank true binders much more robustly than read count-based approaches but also that it yields scores, which accurately capture the underlying SAR. The proposed method can, thus, be of significant utility in hit prioritization following DEL screens.

On the basis of a set of machine learning predictions of glass formation in the Ni–Ti–Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full width at half maximum (fwhm) of >0.42 ?^–1 for the first sharp X-ray diffraction peak. Proper phase labeling is important for future machine learning efforts. We demonstrate that the fwhm and corrosion resistance are correlated but that, while chemistry still plays a role in corrosion resistance, a large fwhm, attributed to a glassy phase, is necessary for the highest corrosion resistance.

Physicochemical property switching of chemical space is of great importance for optimization of compounds, for example, for biological activity. Cyclization is a key method to control 3D and other properties. A two-step approach, which involves a multicomponent reaction followed by cyclization, is reported to achieve the transition from basic moieties to charge neutral cyclic derivatives. A series of multisubstituted oxazolidinones, oxazinanones, and oxazepanones as well as their thio and sulfur derivatives are synthesized from readily available building blocks with mild conditions and high yields. Like a few other methods, MCR and cyclization allow for the collective transformation of a large chemical space into a related one with different properties.

Current commercial fuel cells operate in acidic media where Pt-containing compositions have been shown to be the best oxygen reduction reaction (ORR) electrocatalysts, due to their facile reaction kinetics and long-term stability under operating conditions. However, with the development of alkaline membranes, alkaline fuel cells have become a potentially viable alternative that offers the possibility of using Pt-free (precious metal-free) electrocatalysts. However, the search for better electrocatalysts can be very effort-consuming, if we intend to test every potential bi- or trimetallic combination. In this work, we have explored the application of physical vapor deposition using a custom-built getter cosputtering chamber to prepare catalyst thin films on glassy carbon electrodes, enabling catalyst compositions to be screened in a combinatorial fashion. The activity of combinations containing Au, Cu, Ag, Rh, and Pd as binary metal catalysts, in alkaline media, was studied using rotating disk electrode (RDE) voltammetry with an exchangeable disk electrode holder. Subsequently, we investigated a composition gradient of Pd–Cu, the best performing bimetallic catalyst thin film identified in the initial screening tests. Our results show the viability of using metal getter cosputtering as a rapid and effective tool for preliminary testing of ORR fuel cell electrocatalysts.

Peptide macrocyclization is typically associated with the development of higher affinity and more protease stable protein ligands, and, as such, is an important tool in peptide drug discovery. Yet, within the context of a diverse library, does cyclization give inherent advantages over linear peptides? Here, we used mRNA display to create a peptide library of diverse ring sizes and?topologies (monocyclic, bicyclic, and linear). Several rounds of in vitro selection against streptavidin were performed and the winning peptide sequences were analyzed for their binding affinities and overall topologies. The effect of adding a protease challenge on the enrichment of various peptides was also investigated. Taken together, the selection output yields insights about the relative abundance of binders of various topologies within a structurally diverse library.

A new coronavirus (CoV) caused a pandemic named COVID-19, which has become a global health care emergency in the present time. The virus is referred to as SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) and has a genome similar (～82%) to that of the previously known SARS-CoV (SARS coronavirus). An attractive therapeutic target for CoVs is the main protease (M^pro) or 3-chymotrypsin-like cysteine protease (3CL^pro), as this enzyme plays a key role in polyprotein processing and is active in a dimeric form. Further, M^pro is highly conserved among various CoVs, and a mutation in M^pro is often lethal to the virus. Thus, drugs targeting the M^pro enzyme significantly reduce the risk of mutation-mediated drug resistance and display broad-spectrum antiviral activity. The combinatorial design of peptide-based inhibitors targeting the dimerization of SARS-CoV M^pro represents a potential therapeutic strategy. In this regard, we have compiled the literature reports highlighting the effect of mutations and N-terminal deletion of residues of SARS-CoV M^pro on its dimerization and, thus, catalytic activity. We believe that the present review will stimulate research in this less explored yet quite significant area. The effect of the COVID-19 epidemic and the possibility of future CoV outbreaks strongly emphasize the urgent need for the design and development of potent antiviral agents against CoV infections.

Combinatorial synthesis of Li-ion batteries has proven extremely powerful in screening complex compositional spaces for next-generation materials. To date, no Na-ion counterpart exists wherein Na-ion cathodes can be synthesized in such a way to be comparable to that obtained in bulk synthesis. Herein, we develop a synthesis route wherein hundreds of milligram-scale powder samples can be made in a total time of 3 days. We focus on materials in the Na–Fe–Mn–O pseudoternary system of high immediate interest. Using a sol–gel method, developed herein, yields both phase-pure combinatorial samples of Na_2/3Fe_1/2Mn_1/2O₂ and NaFe_1/2Mn_1/2O₂, consistent with previous reports on bulk samples of interest commercially. By contrast, the synthesis route used for Li-ion cathodes (namely coprecipitations) does not yield phase pure materials, suggesting that the sol–gel method is more effective in mixing the Na, Fe, and Mn than coprecipitation. This has important consequences for all attempts to make these materials, even in bulk. Finally, we demonstrate that these milligram-scale powder samples can be tested electrochemically in a combinatorial cell. The resulting cyclic voltammograms are in excellent agreement with those found on bulk samples in the literature. This demonstrates that the methodology developed here will be effective in characterizing the hundreds of samples needed to understand the complex ternary systems of interest and that such results will scale-up well to the gram and kilogram scale.