Computational assessment of synthetic procedures

Synthetic chemistry is hard because some reasonable looking molecules cannot be made, because there are errors in the chemical literature, because it is easy to miss reaction possibilities and because even the shape of molecules is very difficult to determine. We propose an approach to the computational analysis of reactions that tries to circumvent these difficulties, by restricting the analysis to simple rules for reactivity that can generate a large number of competing pathways. This huge ensemble is filtered using computational methods to pick out the most likely pathways, and to suggest possible products.     

Preparation,characterization and use of new lignocellulose-based bio nanocomposite as a heterogeneous catalyst for sustainable synthesis of pyrimido benzazoles

A sustainable combinatorial synthesis of densely substituted pyrimido [1,2-b] benzazole derivatives in water under microwave irradiation was performed using a new lignocellulose-based bio nanocomposite (BNC) as heterogeneous catalyst. The lignocellulosic waste peanut shells (LCWPS) were turned into a value-added product, a new BNC PS/ZnO, which was prepared via in situ hydrothermal synthesis. The as-prepared BNC was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy and X-ray diffraction spectrum. PS/ZnO has been successfully used in a sustainable catalytic method for the synthesis of pyrimido [1, 2-b] benzazole derivatives in water under microwave irradiation. The time of this reaction was significantly reduced. This catalytic system has a very high turnover number (TON?～?10³) and turnover frequency (TOF?～?10^5?h^?1). This paper presents the benefit of sustainable management of LCWPS, a bio-sourced polymeric carbohydrate for production of new nanocatalyst.     

High-Performance Chemical Information Database towards Accelerating Discovery of Metal-Organic Frameworks for Gas Adsorption with Machine Learning

Chemical structure searching based on databases and machine learning has attracted great attention recently for fast screening materials with target functionalities. To this end, we established a high-performance chemical structure database based on MYSQL engines, named MYDB. More than 160000 metal-organic frameworks (MOFs) have been collected and stored by using new retrieval algorithms for efficient searching and recommendation. The evaluations results show that MYDB could realize fast and efficient keyword searching against millions of records and provide real-time recommendations for similar structures. Combining machine learning method and materials database, we developed an adsorption model to determine the adsorption capacitor of metal-organic frameworks toward argon and hydrogen under certain conditions. We expect that MYDB together with the developed machine learning techniques could support large-scale, low-cost, and highly convenient structural research towards accelerating discovery of materials with target functionalities in the field of computational materials research.     

Synthesis of High Performance Thiophene–Aromatic Polyesters from Bio-Sourced Organic Acids and Polysaccharide-Derived Diol: Characterization and Degradability Studies

In this work, the feasibility of replacing petroleum-based poly(ethylene terephthalate) (PET) with fully bio-based copolyesters derived from dimethyl 2,5-thiophenedicarboxylate (DMTD), dimethyl 2,5-dimethoxyterephthalate (DMDMT), and polysaccharide-derived 1,6-hexanediol (HDO) was investigated. A systematic study of structure-property relationship revealed that the properties of these poly(thiophene–aromatic) copolyesters (PHS(20–90)) can be tailored by varying the ratio of diester monomers in the reaction, whereby an increase in DMTD content noticeably shortened the reaction time in the transesterification step due to its higher reactivity as compared with DMDMT. The copolyesters had weight-average molar masses (Mw) between 27,500 and 38,800 g/mol, and dispersity Đ of 2.0–2.5. The different polarity and stability of heterocyclic DMTD provided an efficient mean to tailor the crystallization ability of the copolyesters, which in turn affected the thermal and mechanical performance. The glass transition temperature (T_g) could be tuned from 70–100 °C, while the tensile strength was in a range of 23–80 MPa. The obtained results confirmed that the co-monomers were successfully inserted into the copolyester chains. As compared with commercial poly(ethylene terephthalate), the copolyesters displayed not only enhanced susceptibility to hydrolysis, but also appreciable biodegradability by lipases, with weight losses of up to 16% by weight after 28 weeks of incubation.     

基于PI法的门源MS6.4地震前震中附近地震热点图像异常变化研究

基于1980年以来青海省和甘肃省区域地震台网的定位地震目录,分析了研究区最小完备震级,选取空间网格尺度为0.2°×0.2°、地震活动异常学习时间段和预测时间段均为3 a,应用图像信息（PI）法,分析了门源M_S6.4地震发生前震中附近地震热点图像的异常变化过程。结果表明,在2013年1月1日至2016年1月1日的预测时间窗内,冷龙岭断裂和祁连山北缘断裂附近存在明显的地震热点,且地震热点图像颜色偏深,发震概率较高,向前滑动至2014年1月1日至2017年1月1日,2015年1月1日至2018年1月1日和2016年1月1日至2019年1月1日3个预测时间窗,此两断裂附近仍存在地震热点,地震热点图像颜色逐渐由深变浅,而门源M_S6.4地震就落在PI法得到的地震热点内。门源M_S6.4地震前存在明显的地震热点,且地震热点相对集中,地震热点图像颜色由深变浅,震后消失;在祁连山北缘断裂附近,地震热点持续存在,分布较集中,地震热点图像颜色也呈由深变浅的过程。未来需关注祁连山北缘断裂,附近有发生强震的可能。     

Prediction of aqueous solubility of compounds based on neural network

Neural network algorithms are gradually applied to the field of chemical informatics. In this paper, the neural network models are used to predict the properties of compounds based on Molecular Information. We predict the aqueous solubility of compounds, and evaluate the prediction results of the Neural Networks including CNN, RNN, DNN, SNN. The performance of the models in predicting the solubility is able to meet or exceed the predicted effect of the method based on the molecular structure (ESOL). DNN model performance is of more accuracy, and RNN performance is of better stability. This method can directly avoid complex molecular structure characterisation, and provide a convenient and flexible way to predict properties of compounds.     

Refolding and simultaneous purification of recombinant human proinsulin from inclusion bodies on protein‐folding liquid‐chromatography columns

Protein‐folding liquid chromatography (PFLC) is an effective and scalable method for protein renaturation with simultaneous purification. However, it has been a challenge to fully refold inclusion bodies in a PFLC column. In this work, refolding with simultaneous purification of recombinant human proinsulin (rhPI) from inclusion bodies from Escherichia coli were investigated using the surface of stationary phases in immobilized metal ion affinity chromatography (IMAC) and high‐performance size‐exclusion chromatography (HPSEC). The results indicated that both the ligand structure on the surface of the stationary phase and the composition of the mobile phase (elution buffer) influenced refolding of rhPI. Under optimized chromatographic conditions, the mass recoveries of IMAC column and HPSEC column were 77.8 and 56.8% with purifies of 97.6 and 93.7%, respectively. These results also indicated that the IMAC column fails to refold rhPI, and the HPSEC column enables efficient refolding of rhPI with a low‐urea gradient‐elution method. The refolded rhPI was characterized by circular dichroism spectroscopy. The molecular weight of the converted human insulin was further confirmed with SDS–18% PAGE, Matrix‐Assisted Laser Desorption/ Ionization Time of Flight Mass Spectrometry (MALDI‐TOF‐MS) and the biological activity assay by HP‐RPLC. Copyright © 2014 John Wiley & Sons, Ltd.     

Biomedical Vibrational Spectroscopy in the Era of Artificial Intelligence

Biomedical vibrational spectroscopy has come of age. The past twenty years have brought many advancements and new developments and now its practitioners face a new challenge: artificial intelligence. Artificial intelligence has the capability to detect meaningful relationships in data sets such as those found in an infrared or Raman spectrum. The present narrative assesses the degree to which biomedical vibrational spectroscopy has already embraced artificial intelligence and what can be expected going forward. This article belongs to the Special Issue Biomedical Applications of Infrared and Raman Spectroscopy.     

Synthesis and properties of bio‐based polyurethanes bearing hydroxy groups derived from alditols

Four kinds of bio‐based polyurethanes bearing hydroxy groups in the pendants were synthesized by the polyaddition of D ‐mannitol‐ and D,L ‐erythritol‐derived diols (1,2:5,6‐di‐O‐isopropylidene‐D ‐mannitol and 1,2‐O‐isopropylidene‐D,L ‐erythritol) with hexamethylene diisocyanate and methyl (S)‐2,6‐diisocyanatohexanoate and the subsequent deprotection of the isopropylidene groups. They were hydrolyzed much more quickly than the corresponding protected polyurethanes at 50 °C and pH 7.0, although their hydrolytic degradation rate was lower than that of polyurethanes with saccharic and glucuronic lactone groups, which had been reported in our previous articles. The introduction of D ‐mannitol units to the polyether‐polyurethanes containing poly(oxytetramethylene) glycol units also enhanced their hydrolyzibility. © 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2011