Hongzong Si

Location of Si Vacancies and [Ti(OSi)4] and [Ti(OSi)3OH] Sites in the MFI Framework: A Large Cluster and Full Ab Initio Study

Titanium silicalite-1 (TS-1) is an important catalyst for selective oxidation reactions. However, the nature and structure of the active sites and the mechanistic details of the catalytic reactions over TS-1 have not been well-understood, leaving a continuous debate on the genesis of active sites on the TS-1 surface in the literature. In this work, the location of Si vacancies and [Ti(OSi)(4)] and [Ti(OSi)(3)OH] sites in the MFI (Framework Type Code of ZSM-5 (Zeolite Socony Mobile-Five)) framework has been studied using a full ab initio method with 40T clusters with a Si:Ti molar ratio of 39:1. It was shown that the former four energetically favorable sites for Si vacancies are T6, T12, T4, and T8 and for Ti centers of [Ti(OSi)(4)] are T10, T4, T8 and T11, being partially the same sites. Whether by replacing Si vacancies or substituting the fully coordinated Si sites, the most preferential site for Ti is T10, which indicates that the insertion mechanism does not affect the favorable sites of Ti in the MFI lattice. For the defective [Ti(OSi)(3)OH] sites, it was found that the Si vacancy at T6 with a Ti at its neighboring T9 site (T6-def-T9-Ti pair) is the most energetically favorable one, followed by a T6-def-T5-Ti pair with a small energy gap. These findings are significant to elucidate the nature of the active sites and the mechanism of reactions catalyzed by TS-1 and to design the TS-1 catalyst.

Origins of Opposite Syn−Anti Diastereoselectivities in Primary and Secondary Amino Acid-Catalyzed Intermolecular Aldol Reactions Involving Unmodified α-Hydroxyketones

The effects of different amino acid catalysts on the stereoselectivity of the direct intermolecular aldol reactions between alpha-hydroxyketones and isobutyraldehyde or 4-nitrobenzaldehyde have been studied with the aid of density functional theory methods. The transition states of the crucial C-C bond-forming step with the enamine intermediate addition to the aldehyde for the proline and threonine-catalyzed asymmetric aldol reactions are reported. B3LYP/6-31+G** calculations provide a good explanation for the opposite syn vs anti diastereoselectivity of these two kinds of amino acid catalysts (anti-selectivity for the secondary cyclic amino acids proline, syn-selectivity for the acyclic primary amino acids like threonine). Calculated and observed diastereomeric ratio and enantiomeric excess values are in good agreement.

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

3D-QSAR modeling and molecular docking study on Mer kinase inhibitors of pyridine-substituted pyrimidines

Mer kinase is a novel therapeutic target for many cancers, and overexpression of Mer receptor tyrosine kinase has been observed in several kinds of tumors. To deeply understand the structure–activity correlation of a series of pyridine/pyrimidine analogs as potent Mer inhibitors, a combined molecular docking and three-dimensional quantitative structure–activity relationship modeling was carried out. A comparative molecular similarity indices analysis model was developed based on the maximum common substructure alignment. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient

Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin

q^{2}

A comparative study of the H + FO (v = 0, j = 0) --> (OH + F)/(HF + O) reaction from QM and QCT methods.

q2 was 0.599, and non-cross-validated

Quantitative structure activity relationship study on EC50 of anti-HIV drugs

r^{2}