A highly effective cobalt catalyst for olefin aziridination with azides: hydrogen bonding guided catalyst design

[Co(P1)], which was designed on the basis of potential hydrogen-bonding interactions in the metal-nitrene intermediate, is a highly active aziridination catalyst with azides. [Co(P1)] can effectively aziridinate various aromatic olefins with arylsulfonyl azides under mild conditions, forming sulfonylated aziridines in excellent yields. The Co-based system enjoys several attributes associated with the relatively low cost of cobalt and the wide accessibility of arylsulfonyl azides. Furthermore, it generates stable dinitrogen as the only byproduct.     

Improved machine learning models for predicting selective compounds

The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance.     

Carbon monoxide poisoning as a probe for the active site(s) of a nickel-based olefin oligomerization catalyst

The interaction of the olefin oligomerization catalyst system derived from [Ni(sacsac)(PBu₃)Cl] (sacsac = pentane-2,4-dithionate = dithioacetylacetonate) with carbon monoxide (CO) has been examined by a combination of ³¹P NMR and FTIR spectroscopy. The catalyst is rapidly and completely inhibited by CO; however, removal of the CO restores catalytic activity. A CO-adduct of the active catalyst has a characteristic CO stretching frequency of 2042 cm^?1, and δ³¹P 9.9 ppm. Carbon monoxide does not react with [Ni(sacsac)(PBu₃)Cl], but [Ni(sacsac)(PBu₃)(Cl)] reacts with any of Et₂AlCl, BuLi, Li[Et₃BH] or K[(s-Bu)₃BH] under an atmosphere of carbon monoxide in the presence or absence of olefin to produce [Ni(PBu₃)(CO)₃], which has been identified by FTIR and ³¹P NMR. [Ni(sacsac)(PBu₃)Cl] reacts completely with BuLi or K[(s-Bu)₃BH] to form catalytically inactive species which yield active catalysts on addition of Et₂AlCl.     

A new efficient iron catalyst for olefin epoxidation with hydrogen peroxide

A new aminopyridine ligand derived from bipiperidine (the product of full reduction of bipyridine, bipy) coordinates to iron(II) in a cis-α fashion, yielding a new selective catalyst for olefin epoxidation with H(2)O(2) under limiting substrate conditions.     

Compass: A shape-based machine learning tool for drug design

Summary Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.     

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

BackgroundDiscover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.MethodsThis paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.ResultsThe proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning.A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.     

Recyclable self-assembly-supported catalyst for chelation-assisted hydroacylation of an olefin with a primary alcohol

[reaction: see text] A novel recyclable catalyst for chelation-assisted hydroacylation of an olefin with a primary alcohol was developed by utilizing a hydrogen-bonding self-assembly motif consisting of a barbiturate bearing 2-aminopyridin-4-yl group and 5-hexyl-2,4,6-triaminopyrimidine. This was further applied to a mixed catalyst system to recycle both organic and organometallic catalysts.     

Kinetics and mechanism of olefin epoxidation with aqueous H2O2 and a highly selective surface-modified TaSBA15 heterogeneous catalyst

The reaction kinetics of cyclohexene epoxidation using aqueous H2O2 oxidant and the highly selective epoxidation catalyst Bu(cap)TaSBA15 were studied. The reaction was determined to be first-order in Ta(V) surface coverage. The reaction rate exhibited saturation with respect to increasing concentrations of cyclohexene and H2O2. An Eley-Rideal mechanism and rate equation may be used to describe the epoxidation kinetics, which are similar to those for Ti(IV)SiO2-catalyzed epoxidations. The observed kinetics may also be modeled by a double-displacement mechanism typically associated with saturation enzyme catalysts. In addition, (1)H NMR spectroscopy was employed to investigate H2O2 decomposition by Bu(cap)TaSBA15 and the unmodified TaSBA15 catalysts. Little decomposition occurred over the surface-modified material, but the unmodified material catalyzed a 30% conversion of H2O2 after 6 h. UV-visible absorbance and diffuse reflectance UV-visible (DRUV-vis) spectroscopy were used to investigate the structure of the Ta centers on the TaSBA15 catalysts. DRUV-vis spectroscopy was also used to identify a Ta(V)-based epoxidation intermediate, proposed to be a Ta(V)(eta(2)-O2) species, which forms upon reaction of the TaSBA15 and Bu(cap)TaSBA15 materials with H2O2.     

Experimental design,machine learning approaches for the optimization and modeling of caffeine adsorption

In the current research, the sorption of caffeine on fresh and calcined Cu–Al layered double hydroxide was comparatively studied based on adsorption parameters, adsorption kinetics, and adsorption isotherm. Response surface methodology (RSM), support vector machine (SVM) and artificial neural network (ANN), as data mining methods, were applied to develop models by considering various operating variables. Different characterization methods were exploited to conduct a comprehensive analysis of the characteristics of HDL in order to acquire a thorough understanding of its structural and functional features. The Langmuir model was employed to accurately describe the maximum monolayer adsorption capacity for calcined sample (q_max) of 152.99 mg/g mg/g with R² = 0.9977. The pseudo-second order model precisely described the adsorption phenomenon (R² = 0.999). The thermodynamic analysis also reveals a favorable and spontaneous process. The ANN model predicts adsorption efficiency result with R² = 0.989. The five-fold cross-validation was achieved to evaluate the validity of the SVM. The predication results revealed approximately 99.9% accuracy for test datasets and 99.63% accuracy for experiment data. Moreover, ANOVA analysis employing the central composite design-response surface methodology (CCD-RSM) indicated a good agreement between the quadratic equation predictions and the experimental data, which results in R² of 0.9868 and the highest removal percentages in optimized step were obtained for RSM (pH 5.05, mass of adsorbent 20 mg, time of 72 min, and caffeine concentrations of 22 mg/L). On the whole, the findings confirm that the proposed machine learning models provided reliable and robust computer methods for monitoring and simulating the adsorption of pollutants from aqueous solutions by Cu–Al–LDH.     

Phosphane-free green protocol for selective nitro reduction with an iron-based catalyst

Iron phthalocyanine with iron sulfate has been successfully applied for high chemo- and regioselective reduction of aromatic nitro compounds to give the corresponding amines in a green solvent system without using any toxic ligand. The catalytic systems were also compatible with a large range of other reducible functional groups, such as keto, acid, amide, ester, halogen, lactone, nitrile, N-benzyl, O-benzyl, hydroxy, and heterocycles. In the present study, dinitro compounds have been regioselectively reduced to the corresponding amines with high yield. In most of the cases the conversion and selectivity was greater than 99% as determined by GC-MS analysis.     

激光诱导击穿光谱对U元素的定量分析

铀矿是核领域最重要的矿产资源之一,快速、有效勘探铀矿资源能促进核领域平稳、健康发展。激光诱导击穿光谱(LIBS)技术具备多目标元素现场快速检测的优点,能实现铀矿资源准确、快速的现场分析。本工作基于LIBS技术对铀矿中U元素进行了定量分析,对比了偏最小二乘(PLS)和随机森林(RF)两种机器学习算法的定量效果。结果显示,RF模型的定量线性相关系数为0.996,对三个验证集的相对误差分别是22.33%、12.79%和12.04%;PLS模型的定量线性相关系数为0.997,对三个验证集的相对误差分别是4.33%、6.63%和6.85%。对比结果表明,本研究中的PLS模型定量准确度更高,同RF算法相比,PLS算法更适用于铀矿中U的LIBS定量分析。     

Half-sandwich molybdenum complexes: molecular structure and catalyst precursors for olefin epoxidation with tert-butyl hydroperoxide

1. Download high-res image (129KB)
2. Download full-size image

     

An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(T_c) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining me...     

Rod-shaped porous alumina-supported Cr2O3 catalyst with low acidity for propane dehydrogenation

Direct catalytic propane dehydrogenation (PDH) to obtain propylene is a more economical and environmentally friendly route for propylene production. In particular, alumina-supported Cr₂O₃ catalysts can have better potential applications if the acidic properties could be tuned. Herein, a series of rod-shaped porous alumina were prepared through a hydrothermal route, followed by calcination. It was found that the acidity of the synthesized alumina was generally lower than that of the commercial alumina and could be adjusted well by varying the calcination temperature. Such alumina materials were used as supports for active Cr₂O₃, and the obtained catalysts could enhance the resistance to coke formation associated with similar activity in PDH reaction compared to the commercial alumina. The amount of coke deposited on a self-made catalyst (Cr-Al-800) was 3.6%, which was much lower than that deposited on the reference catalyst (15.7%). The lower acidity of the catalyst inhibited the side reactions and coke formation during the PDH process, which was beneficial for its high activity and superior anti-coking properties.     

Sn-MCM-41--a heterogeneous selective catalyst for the Baeyer-Villiger oxidation with hydrogen peroxide

A new heterogeneous catalyst, Sn-MCM-41, is described for the Baeyer-Villiger reaction with hydrogen peroxide which selectively activates the carbonyl function for the nucleophilic attack by the oxidant, with high chemoselectivities when double bonds are present in the molecule.