Computational Method Investigation of Solid Ducted Rocket

This paper proposes a standardized format for the preparation of process green synthesis reports that can be applied to chemical syntheses of active pharmaceutical ingredients (APIs) of importance to the pharmaceutical industry. Such a report is comprised of the following eight sections: a synthesis scheme, a synthesis tree, radial pentagons and step E-factor breakdowns for each reaction step, a tabular summary of key material efficiency step and overall metrics for a synthesis plan, a mass process block diagram, an energy consumption audit based on heating and cooling reaction and auxiliary solvents, a summary of environmental and safety-hazard impacts based on organic solvent consumption using the Rowan solvent greenness index, and a cycle time process schedule. Illustrative examples of process green synthesis reports are given for the following pharmaceuticals: 5-HT2B and 5-HT7 receptors antagonist (Astellas Pharma), brivanib (Bristol-Myers Squibb), and orexin receptor agonist (Merck). Methods of ranking synthesis plans to a common target product are also discussed using 6 industrial synthesis plans of apixaban (Bristol-Myers Squibb) as a working example. The Borda count method is suggested as a facile and reliable computational method for ranking multiple synthesis plans to a common target product using the following 4 attributes obtained from a process green synthesis report: process mass intensity, mass of sacrificial reagents used per kg of product, input enthalpic energy for solvents, and Rowan solvent greenness index for organic solvents.<br>

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Five-Lump Kinetic Model for the Catalytic Cracking Process\

Revista de Chimie ◽

10.37358/rc.18.10.6595 ◽

2018 ◽

Vol 69 (10) ◽

pp. 2633-2637

Author(s):

Raluca Dragomir ◽

Paul Rosca ◽

Cristina Popa

Keyword(s):

Kinetic Model ◽

Programming Language ◽

Catalytic Cracking ◽

Computational Method ◽

Industrial Data ◽

New Model ◽

Optimization And Control ◽

Cracking Process ◽

And Control ◽

Matlab Programming

The main objectives of the present paper are to adaptation the five-kinetic model of the catalytic cracking process and simulation the riser to predicts the FCC products yields when one of the major input variable of the process is change. The simulation and adaptation are based on the industrial data from Romanian refinery. The adaptation is realize using a computational method from Optimization Toolbox from Matlab programming language. The new model can be used for optimization and control of FCC riser.

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Stripping of Real Estate-Indexed Swaps and Forward Term Structure: Interest and Computational Method

SSRN Electronic Journal ◽

10.2139/ssrn.2559215 ◽

2011 ◽

Cited By ~ 2

Author(s):

Pierre-Arnaud Henri Drouhin ◽

Arnaud Simon

Keyword(s):

Real Estate ◽

Term Structure ◽

Computational Method

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Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence

Protein and Peptide Letters ◽

10.2174/0929866526666191002111404 ◽

2020 ◽

Vol 27 (3) ◽

pp. 178-186 ◽

Cited By ~ 2

Author(s):

Ganesan Pugalenthi ◽

Varadharaju Nithya ◽

Kuo-Chen Chou ◽

Govindaraju Archunan

Keyword(s):

Random Forest ◽

Protein Sequence ◽

Glycosylation Site ◽

Computational Method ◽

The Other ◽

Eukaryotic Protein ◽

Random Forest Method ◽

Glycosylation Sites ◽

Human And Mouse ◽

Better Than

Background: N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. Objective: In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. Methods: In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. Results: Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. Conclusion: Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.

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A Computational Method for the Identification of Endolysins and Autolysins

Protein and Peptide Letters ◽

10.2174/0929866526666191002104735 ◽

2020 ◽

Vol 27 (4) ◽

pp. 329-336 ◽

Cited By ~ 1

Author(s):

Lei Xu ◽

Guangmin Liang ◽

Baowen Chen ◽

Xu Tan ◽

Huaikun Xiang ◽

...

Keyword(s):

Support Vector Machine ◽

Cell Wall ◽

Experimental Results ◽

Computational Method ◽

Lytic Enzyme ◽

Support Vector ◽

Lytic Enzymes ◽

Data Set ◽

Optimal Feature ◽

Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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