Combinatorial Chemistry & High Throughput Screening - Volume 21, Issue 9, 2018
Volume 21, Issue 9, 2018
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Catalytic Conversion of Biorenewable Sugar Feedstocks into Market Chemicals
Authors: Gary Diamond, Alfred Hagemeyer, Vince Murphy and Valery SokolovskiiThe transformation of low cost sugar feedstocks into market chemicals and monomers for existing or novel high performance polymers by chemical catalysis is reviewed. Emphasis is given to industrially relevant, continuous flow, trickle bed processes. Since long-term catalyst stability under hydrothermal conditions is an important issue to be addressed in liquid phase catalysis using carbohydrate feedstocks, we will primarily discuss the results of catalytic performance for prolonged times on stream. In particular, the selective aerobic oxidation of glucose to glucaric acid and the subsequent selective hydrogenation to adipic acid is reviewed. Hydroxymethylfurfural (HMF), which is readily available from fructose, can be upgraded by oxidation to furan dicarboxylic acid (FDCA) or by consecutive reduction and hydrogenolysis to hexanetriol (HTO) followed by hydrogenolysis to biobased hexanediol (HDO). Direct amination of HDO yields biobased hexamethylene diamine (HMDA). Aerobic oxidation of HDO represents an alternative route to biobased adipic acid. HMDA and adipic acid are the monomers required for the production of nylon- 6,6, a major polymer for engineering and fibre applications.
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An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data
Authors: Saeed Ahmed, Muhammad Kabir, Zakir Ali, Muhammad Arif, Farman Ali and Dong-Jun YuAim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.
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A Novel, Fast and Efficient One-Pot Three-Component Procedure for the Preparation of New Imidazolidinone Derivatives from Isocyanide, Aldehyde and Urea
Authors: Hamidreza Safaei, Neda Firoozi, Mahboobeh Zebarjadian, Seyed A. Jehbez and Maryam SafaeiBackground: Multicomponent processes have played powerful roles in achieving complex structures, which are also aligned with green chemistry. Thus, MCRs have attracted considerable interest due to their atom economy, simple experimental procedures, automated synthesis, convenience and synthetic efficiency. Isocyanides are one of the crucial starting material in designing MCRs methods. They are unique building blocks in many cycloaddition reactions since they are able to react with both nucleophiles and electrophiles at the same carbon. Furthermore, ammonium chloride is an inorganic compound that is highly soluble in water, inexpensive and commercially available. Solutions of ammonium chloride are mildly acidic and have been used in various reactions. Objective: This article focuses on design a convenient and straightforward method for assembling important scaffolds such imidazolidinones through one-pot three-component strategy. Conclusion: The straightforward and efficient one-pot three component method for the synthesis of 4-(cyclohexylmethylene)-5-phenylimidazolidin-2-one derivatives is described. This reaction exploits the formation of imine, which undergoes spontaneous intermolecular cycloaddition with isocyanide, and generates the desired products in a good yield.
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Prognostic Impact of Lymphadenectomy in Different Stages of Malignant Germ Cell Tumor of the Ovary Based on Propensity Score Matching
Authors: Ying Chen, Yang Ning, Qinghua Zhang and Ying XieBackground: Lymphadenectomy has been widely used in the treatment of malignant germ cell tumor of the ovary (OGCT), which is a kind of ovarian cancers occurred mostly in young women and adolescent girls. But the clinical decision mainly depends on the doctor’s experience without a well-defined guideline. This population-based study aimed to evaluate the prognostic impact of lymphadenectomy in different stages of malignant germ cell tumors of the ovary. Methods: Patients with known status of lymphadenectomy in different stages of OGCT were explored from the Surveillance, Epidemiology, and End Results (SEER) program database from 1973 to 2013. We used propensity score matching algorithm to reduce the selection bias between the two study groups. Survival curves, univariate and multivariate Cox proportional hazards model were applied to evaluate the prognostic impact of lymphadenectomy in different stages of OGCT. Results: We included 1,996 OGCT patients in the study, and 818 (41%) of them had lymph node resection. Compared to the LND- group, patients with lymph node resection tended to be at stage II and III, had larger tumor sizes and diagnosed as dysgerminoma. The influence of diagnosis ages, marital status and tumor grades were significantly decreased by applying the propensity score matching. Lymphadenectomy-positive (LND+) group demonstrated significantly worse survival than the lymphadenectomy-negative (LND-) group in later stages (stage III, overall, P=0.027, cancerspecific, P=0.006; stage IV, overall, P=0.034, cancer-specific, P=0.037). While, both the overall and cancer-specific survival showed no significant differences between LND+ and LND- in stage I (overall, P=0.411, cancer-specific, P=0.876) and stage II (overall, P=12, cancer-specific, P=0.061). Univariate (overall, HR=1.497, CI=1.010-2.217, P=0.044; cancer-specific, HR=1.524, CI=1.067- 2.404, P=0.050) and multivariate (overall, HR=1.580, CI=1.046-2.387, P=0.030; cancer-specific, HR=1.661, CI=1.027-2.686, P=0.039) Cox proportional model both verified the association between the lymph node resection and better survival in the whole cohort. Conclusion: Lymphadenectomy significantly increased the survival probability of OGCT patients in stage III and IV, but had no significant influence on early-stage patients (stage I and II), indicating lymphadenectomy should be performed in a stage-dependent manner in clinical utility.
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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning
Authors: Junnan Zhao, Lu Zhu, Weineng Zhou, Lingfeng Yin, Yuchen Wang, Yuanrong Fan, Yadong Chen and Haichun LiuBackground: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.
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A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information
Authors: Zi-Han Guo, Lei Chen and Xian ZhaoAim and Objective: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been uncovered. However, their completeness is still on the way. A number of prediction methods have been built to assign chemicals into certain metabolic pathway, which can further be used to predict novel latent chemicals for a given metabolic pathway. However, they did not make use of chemical properties in a system level to construct prediction models. Method: In this study, we applied a network integration method, which can extract topological features from different chemical networks, representing chemical associations from their different properties, and fused several high-dimension vector representations into a low-dimension vector representation for each chemical. The compact vector representations were fed into the Support Vector Machine (SVM) to construct the prediction model. To tackle the problem that one chemical can participate in more than one pathway type, we construct an SVM-based binary prediction model for each pathway type to determine whether a given chemical can participate in the pathway type. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) was adopted to weaken the influence of imbalanced dataset. Results and Conclusion: Each binary model gave a quite good performance and was superior to the classic prediction model, indicating that the proposed models can be useful tools for integrating heterogeneous information to assign chemicals into correct metabolic pathways.
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An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity
Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.
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Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size
Authors: A. A. Öztürk, A. Bilge Gündüz and Ozan OzisikAims and Objectives: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric. Materials and methods: SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated. Results: PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods. Conclusion: Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.
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Volumes & issues
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Volume 28 (2025)
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Volume 27 (2024)
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Volume 26 (2023)
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Volume 25 (2022)
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Volume 24 (2021)
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Volume 23 (2020)
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Volume 22 (2019)
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Volume 21 (2018)
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Volume 20 (2017)
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Volume 19 (2016)
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Volume 18 (2015)
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Volume 17 (2014)
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Volume 16 (2013)
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Volume 15 (2012)
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Volume 14 (2011)
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Volume 13 (2010)
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Volume 12 (2009)
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Volume 11 (2008)
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Volume 10 (2007)
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Volume 9 (2006)
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Volume 8 (2005)
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Volume 7 (2004)
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Volume 6 (2003)
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Volume 5 (2002)
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Volume 4 (2001)
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Volume 3 (2000)
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