Computers & chemistry最新文献

{"title":"Automation of a procedure to find the polynomial which best fits (κ, c1, c2, T) data of electrolyte solutions by non-linear regression analysis using mathematica® software","authors":"E Cortazar,&nbsp;A Usobiaga,&nbsp;L.A Fernández,&nbsp;A de Diego,&nbsp;J.M Madariaga","doi":"10.1016/S0097-8485(01)00115-2","DOIUrl":"10.1016/S0097-8485(01)00115-2","url":null,"abstract":"<div><p>A <span>mathematica</span>® package, ‘<span>condu.m’</span>, has been developed to find the polynomial in concentration and temperature which best fits conductimetric data of the type (<em>κ</em>, <em>c</em>, <em>T</em>) or (<em>κ</em>, <em>c</em>₁, <em>c</em>₂, <em>T</em>) of electrolyte solutions (<em>κ</em>: specific conductivity; <em>c</em>_<em>i</em>: concentration of component <em>i</em>; <em>T</em>: temperature). In addition, an interface, ‘<span>tkondu’</span>, has been written in the TCL/Tk language to facilitate the use of <span>condu.m</span> by an operator not familiarised with <span>mathematica</span>®. All this software is available on line (<span>UPV/EHU, 2001</span>). ‘<span>condu.m</span>’ has been programmed to: (i) select the optimum grade in <em>c</em>₁ and/or <em>c</em>₂; (ii) compare models with linear or quadratic terms in temperature; (iii) calculate the set of adjustable parameters which best fits data; (iv) simplify the model by elimination of ‘a priori’ included adjustable parameters which after the regression analysis result in low statistical significance; (v) facilitate the location of outlier data by graphical analysis of the residuals; and (vi) provide quantitative statistical information on the quality of the fit, allowing a critical comparison among different models. Due to the multiple options offered the software allows testing different conductivity models in a short time, even if a large set of conductivity data is being considered simultaneously. Then, the user can choose the best model making use of the graphical and statistical information provided in the output file. Although the program has been initially designed to treat conductimetric data, it can be also applied for processing data with similar structure, e.g. (<em>P</em>, <em>c</em>, <em>T</em>) or (<em>P</em>, <em>c</em>₁, <em>c</em>₂, <em>T</em>), being <em>P</em> any appropriate transport, physical or thermodynamic property.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 253-264"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00115-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79282498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QSPR models derived for the kinetic data of the gas-phase homolysis of the carbon–methyl bond","authors":"Rein Hiob,&nbsp;Mati Karelson","doi":"10.1016/S0097-8485(01)00112-7","DOIUrl":"10.1016/S0097-8485(01)00112-7","url":null,"abstract":"<div><p>A quantitative structure–property relationship study was carried out on the kinetic parameters of the gas-phase homolysis for 58 different CCH₃ bonds using the <span>codessa</span> program. Six-parameter models were developed for the prediction of the log<!--> <em>k</em> (1047 K) and the parameters of the Arrhenius equation, log<!--> <em>A</em> and <em>E</em>. These correlations were obtained by employing the theoretical molecular descriptors, derived from only the information encoded in the chemical structure of compounds.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 237-243"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00112-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78398245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel atom-type AI indices for QSPR studies of alcohols","authors":"Biye Ren","doi":"10.1016/S0097-8485(01)00111-5","DOIUrl":"10.1016/S0097-8485(01)00111-5","url":null,"abstract":"<div><p>The novel vertex degree <em>v</em>^m for heteroatom in molecular graph is derived on the basis of the valence connectivity <em>δ</em>^v of Kier–Hall. The newly proposed atom-type AI indices and previously proposed Xu index, are further modified for compounds with heteroatoms by replacing the vertex-degree of heteroatom by the proposed <em>v</em>^m. The multiple linear regression using the modified Xu index and AI indices can provide high-quality QSPR models for the normal boiling points (BP), molar volumes (MV), molar refractions (MR), and molecular total surface areas (TSA) of alcohols with up to 17 non-hydrogen atoms. The results imply that these physical properties may be expressed as a linear combination of the individual indices related to molecular size and atom-types. For each of the four properties, the correlation coefficient <em>r</em> is greater than 0.996 and particularly the decrease in the standard error is within the range of 61–83% compared with the simple linear models based on the modified Xu index, and the standard errors are 3.814, 0.939, 0.187, and 3.348 for BP, MV, MR, and TSA, respectively. The final models correspond to a fit error of 2.33, 0.70, 0.53, and 0.95% for BP, MV, MR, and TSA, respectively. The more general leave-<em>n</em>-out method is used to do the cross-validation. The cross-validation demonstrates the outstanding predictive power of the final models. The contributions of individual indices are used to illustrate the role of the molecular size and individual groups in molecules. The results indicate that physical properties of alcohols are dominated by the molecular size. On the other hand, although the hydrogen-bonding interactions caused by the OH group play an important role in determining the normal BPs, the branching seems to be a more important factor influencing the MVs, MRs, and TSAs than the hydrogen-bonding interaction. The contribution of individual atom type or group to properties is not a constant and depends on its structural environment in a molecule.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 223-235"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00111-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73266781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Note from the Editor and Publisher","authors":"","doi":"10.1016/S0097-8485(01)00129-2","DOIUrl":"https://doi.org/10.1016/S0097-8485(01)00129-2","url":null,"abstract":"","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Page 193"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00129-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136717508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a Euclid distance discriminant method to find protein coding genes in the yeast genome","authors":"Chun-Ting Zhang ,&nbsp;Ju Wang ,&nbsp;Ren Zhang","doi":"10.1016/S0097-8485(01)00107-3","DOIUrl":"10.1016/S0097-8485(01)00107-3","url":null,"abstract":"<div><p>The Euclid distance discriminant method is used to find protein coding genes in the yeast genome, based on the single nucleotide frequencies at three codon positions in the ORFs. The method is extremely simple and may be extended to find genes in prokaryotic genomes or eukaryotic genomes with less introns. Six-fold cross-validation tests have demonstrated that the accuracy of the algorithm is better than 93%. Based on this, it is found that the total number of protein coding genes in the yeast genome is less than or equal to 5579 only, about 3.8–7.0% less than 5800–6000, which is currently widely accepted. The base compositions at three codon positions are analyzed in details using a graphic method. The result shows that the preference codons adopted by yeast genes are of the R<span><math><mtext>G</mtext><mtext>̄</mtext></math></span>W type, where R, <span><math><mtext>G</mtext><mtext>̄</mtext></math></span> and W indicate the bases of purine, non-G and A/T, whereas the ‘codons’ in the intergenic sequences are of the form NNN, where N denotes any base. This fact constitutes the basis of the algorithm to distinguish between coding and non-coding ORFs in the yeast genome. The names of putative non-coding ORFs are listed here in detail.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 195-206"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00107-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87604469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of protein structural classes by support vector machines","authors":"Yu-Dong Cai ,&nbsp;Xiao-Jun Liu ,&nbsp;Xue-biao Xu ,&nbsp;Kuo-Chen Chou","doi":"10.1016/S0097-8485(01)00113-9","DOIUrl":"10.1016/S0097-8485(01)00113-9","url":null,"abstract":"<div><p>In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino acid composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 293-296"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00113-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"56174795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post Hartree–Fock and density functional theory studies on structure and conformational stability of nitrosoethylene and substituted compounds of nitrosoethylene","authors":"K Senthilkumar,&nbsp;P Kolandaivel","doi":"10.1016/S0097-8485(01)00109-7","DOIUrl":"10.1016/S0097-8485(01)00109-7","url":null,"abstract":"<div><p>Post Hartree–Fock and density functional theory (DFT) methods were used to study the different conformers of nitrosoethylene HCHCHNO, and substituted compounds of the nitrosoethylene RCHCHNO (R=Cl, NH₂, N(CH₃)₂, OH, OCH₃). The molecules were optimized at MP2/6-31G* level of theory of ab initio and B3LYP/6-31G* and B3PW91/6-31G* levels of theory of DFT. Special emphasis has been given to the effect of substitution of π-electron donor groups NH₂, N(CH₃)₂, OH, and OCH₃, which play a major role in modifying the geometrical parameters of NO group by the electronic transmission effects through the central group CHCH. The ability of DFT methods to predict the bond length adjacent to the atoms having lone pair electrons has been discussed. The conformational stabilities have been studied using the relative energies and DFT parameters such as chemical hardness and chemical potential. The role of intra-molecular hydrogen bond on the equilibrium structure has been discussed. The vibrational spectra for the different conformers of the nitrosoethylene and substituted compounds have been generated using the MP2/6-31G* level of theory.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 207-221"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00109-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77581074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a quantitative structure–property relationship model for predicting the electrophoretic mobilities","authors":"Qianfeng Li ,&nbsp;Lijun Dong ,&nbsp;Runping Jia ,&nbsp;Xingguo Chen ,&nbsp;Zhide Hu ,&nbsp;B.T Fan","doi":"10.1016/S0097-8485(01)00114-0","DOIUrl":"10.1016/S0097-8485(01)00114-0","url":null,"abstract":"<div><p>Electrophoretic mobility (<em>μ</em>₀) is the most important parameter governing the separation of solutes in capillary zone electrophoresis. In this paper, a new model was constructed by means of a multilayer neural network using extended delta-bar-delta (EDBD) algorithm to estimate complex property of electrophoretic mobilities of aliphatic carboxylates and amines from simpler experimental properties. The molecular weight (<em>W</em>), molecular volume (<em>V</em>), the code (+1 or −1) of acid and base and p<em>K</em> value were used as input parameters to predict electrophoretic mobility. The networks' architecture and the learning times were optimized. The optimum artificial neural networks (ANNs) could give excellent prediction results.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 3","pages":"Pages 245-251"},"PeriodicalIF":0.0,"publicationDate":"2002-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00114-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89041257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Fraction-based Kinetic Model for Simulating Hydrodesulfurization Process of Petroleum Fraction","authors":"Weixiang Zhao, Dezhao Chen, Shangxu Hu","doi":"10.1016/S0097-8485(01)00091-2","DOIUrl":"https://doi.org/10.1016/S0097-8485(01)00091-2","url":null,"abstract":"","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"58 1","pages":"141-8"},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80977400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P-stable Eighth Algebraic Order Methods for the Numerical Solution of the Schrödinger Equation","authors":"A. Konguetsof, T. Simos","doi":"10.1016/S0097-8485(01)00085-7","DOIUrl":"https://doi.org/10.1016/S0097-8485(01)00085-7","url":null,"abstract":"","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"16 1","pages":"105-11"},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82207046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}