Computers & chemistry最新文献

Stoichiometrically, exact candidate pathways or mechanisms for deriving the rate law of a catalytic or complex reaction can be determined through the synthesis of networks of plausible elementary reactions constituting such pathways. A rigorous algorithmic method is proposed for executing this synthesis, which is exceedingly convoluted due to its combinatorial complexity. Such a method for synthesizing networks of reaction pathways follows the general framework of a highly exacting combinatorial method established by us for process-network synthesis. It is based on the unique graph-representation in terms of P-graphs, a set of axioms, and a group of combinatorial algorithms. In the method, the inclusion or exclusion of a step of each elementary reaction in the mechanism of concern hinges on the general combinatorial properties of feasible reaction networks. The decisions are facilitated by solving linear programming problems comprising a set of mass-balance constraints to determine the existence or absence of any feasible solution. The search is accelerated further by exploiting the inferences of preceding decisions, thereby eliminating redundancy. As a result, all feasible independent reaction networks, i.e. pathways, are generated only once; the pathways violating any first principle of either stoichiometry or thermodynamics are eliminated. The method is also capable of generating those combinations of independent pathways directly, which are not microscopically reversible. The efficiency and efficacy of the method are demonstrated with the identification of the feasible mechanisms of ammonia synthesis involving as many as 14 known elementary reactions.

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 codessa program. Six-parameter models were developed for the prediction of the log k (1047 K) and the parameters of the Arrhenius equation, log A and E. These correlations were obtained by employing the theoretical molecular descriptors, derived from only the information encoded in the chemical structure of compounds.

The novel vertex degree v^m for heteroatom in molecular graph is derived on the basis of the valence connectivity δ^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 v^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 r 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-n-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.

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$\text{G}\text{̄}$W type, where R, $\text{G}\text{̄}$ 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.

Electrophoretic mobility (μ₀) 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 (W), molecular volume (V), the code (+1 or −1) of acid and base and pK 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.

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.

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.