Computers & chemistry最新文献

Comparative molecular field analysis (CoMFA) has been widely used as a standard three dimensional quantitative structure–activity relationship (3D-QSAR) method. Although CoMFA is a useful technique, it does not always reflect real ligand–receptor interaction. Molecular interactions between the ligand and receptor are mainly occurred near the van der Waals surface of ligand. All grid points surrounding whole molecule in CoMFA are not important as molecular descriptors. If each molecule is represented by physico-chemical parameters on molecular surface, more precise and realistic 3D-QSAR is possible. We developed a new surface-based 3D-QSAR method using Kohonen neural network (KNN) and three-way partial least squares (3-way PLS). This method was applied to 25 dopamine 2 (D2) receptor antagonists for validation. First, the 3D coordinates of all sampling points on the van der Waals surface were projected into the 2D map by KNN. Each node in the map was coded by the associated molecular electrostatic potential (MEP) value of the original sampling point. Then, the correlation between the MEP values of all 2D maps and D2 receptor antagonist activities was analyzed by 3-way PLS. The statistics of the 3-way PLS model was excellent and the coefficients back-projected on the van der Waals surface had reasonable 3D distribution. Lastly, all data was divided into the calibration and validation sets by D-optimal designs and the activities of validation set were predicted. The external validation suggested that 3-way PLS is better than standard (2-way) PLS for prediction.

The aim of this paper is to present a procedure that utilizes ¹³C NMR for identification of substituent groups which are bonded to carbon skeletons of natural products. For so much was developed a new version of the program macrono, that presents a database with 161 substituent types found in the most varied terpenoids. This new version was widely tested in the identification of the substituents of 60 compounds that, after removal of the signals that did not belong to the carbon skeleton, served to test the prediction of skeletons by using other programs of the expert system SISTEMAT.

The fourth order accuracy of the spatial discretisation of time-dependent reaction–diffusion equations, in finite-difference electrochemical kinetic simulations in one space dimension, might well be achieved by means of the three-point Numerov method, instead of the 5(6)-point discretisation of second spatial derivatives, recently suggested in the literature. This is proven theoretically, and tested in simulations of potential-step chronoamperometric and current-step chronopotentiometric transients for the Reinert–Berg system, which is a classical example of electrochemical reaction–diffusion equations. Although less generally applicable than the 5(6)-point spatial scheme, the Numerov discretisation is easier to use, because it does not lead to increased linear equation matrix bandwidth, but results in quasi-block-tridiagonal matrices, similar to those for the conventional, second order accurate, three-point spatial discretisation. The simulations reveal that the Numerov method brings an improvement of accuracy and efficiency that is comparable with the one offered by the 5(6)-point spatial scheme.

para-Xylene is widely used in chemical industry. It can be synthesized by alkylation of toluene with methanol using zeolite ZSM-5 as catalyst. The proportion of para-xylene, among its other isomers and other reaction byproducts, depends on the reaction conditions. As this process still remains largely empirical, we attempted to build a theoretical model able to predict the para-xylene yield under specific reaction conditions. We have consequently collected data regarding this reaction from the literature and exploited the potency of a particular artificial neural network (ANN), the counter-propagation ANN based on the Kohonen technique. The results show that such an approach is suitable to establish a predictive model of the yield in para-xylene on the basis of reaction parameters. The quality of the model could be further improved by considering a larger valuable data set, e.g. including experiments characterized by a low yield in para-xylene.

This paper addresses the efficient solution of computer aided molecular design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branching functions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the design of optimal solvents to be used as cleaning agents in lithographic printing.

Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2⁴ full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl₂ flow rate. The other called multipole ICP was characterized by performing a 3³ full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density, electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.

Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand–receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand–receptor interaction energy is developed from known ligand–receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand–receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.

The aim of this work is to construct a tool to assist in the prediction of peptidic properties resulting from the exchange of two amino acids in a proteic chain. In the past others have used experimental properties for this purpose. However, the nature of these data sets severely limits their access to important properties pertaining to secondary structure, and hence the indices used cannot characterize different backbone conformers like α helix and β strands, or side-chain conformations like gauche+, gauche− and trans. In this study we explore the importance of backbone and side-chain angles with regard to conformer similarity measured with theoretical properties calculated in an ab initio manner. For each of the 20 genetically encoded amino acids, we studied five conformers that correspond to α helical and β strand structures, with three different side chain conformations for each, defined solely by their angles Φ, Ψ and χ₁. This methodology allowed each of the 108 conformers to be represented by a mathematical object without ambiguity. The peptidic chain was emulated using two capping models to simulate the effect of nearest neighbors. These are OHCX_aaNH₂ and AlaX_aaAla, where X_aa is the conformer of interest. We then calculated 40 ab initio quantum chemical and graph theory indices for each backbone-side-chain conformer to obtain a characterization and classification scheme. We found that: (1) while backbone structure is very important to conformer similarity, side-chain conformations do not cluster together in a top-level manner; (2) amino acids with π electrons group together independent of backbone conformation.

A new approach for eliminating the redundant variables in the multivariable data matrix encountered in QSAR studies, minor latent variable perturbation (MLVP)-PLS method has been proposed. In the latent variable (LV) space, the minor latent variables (LVs) with small covariances are mainly formulated by linear combinations of the redundant variables including information-deficient and highly correlative ones, while the major LVs with large covariances are mainly contributed by the informative variables. Deleting a minor LV, which is equivalent to a perturbation for LV space, could make the redundant variables not well be represented in LV subspace, leading to strong variation of their PLS regression coefficients. The informative variables could still be normally represented in LV subspace with the PLS regression coefficients remaining relatively stable. MLVP-PLS utilizes this fact to discriminate the informative and redundant variables. It gradually identifies and eliminates the redundant variables according to the relative variation of PLS regression coefficients after perturbations are given. The elimination process is terminated according to some proposed criteria. Applying the method to the quantitative structure–activity relationship (QSAR) studies on TIBO derivatives as potential anti-HIV drugs has demonstrated the feasibility and robustness of the proposed approach. A deeper insight into the effect of different structural parameters on the bio-activity of TIBO derivatives has been reached.

We examined more than 700 DNA sequences (full length chromosomes and plasmids) for stretches of purines (R) or pyrimidines (Y) and alternating YR stretches; such regions will likely adopt structures which are different from the canonical B-form. Since one turn of the DNA helix is roughly 10 bp, we measured the fraction of each genome which contains purine (or pyrimidine) tracts of lengths of 10 bp or longer (hereafter referred to as ‘purine tracts’), as well as stretches of alternating pyrimidines/purine (‘pyr/pur tracts’) of the same length. Using this criteria, a random sequence would be expected to contain 1.0% of purine tracts and also 1.0% of the alternating pyr/pur tracts. In the vast majority of cases, there are more purine tracts than would be expected from a random sequence, with an average of 3.5%, significantly larger than the expectation value. The fraction of the chromosomes containing pyr/pur tracts was slightly less than expected, with an average of 0.8%. One of the most surprising findings is a clear difference in the length distributions of the regions studied between prokaryotes and eukaryotes. Whereas short-range correlations can explain the length distributions in prokaryotes, in eukaryotes there is an abundance of long stretches of purines or alternating purine/pyrimidine tracts, which cannot be explained in this way; these sequences are likely to play an important role in eukaryotic chromosome organisation.