6-loop anomalous dimension of a single impurity operator from AdS/CFT and multiple zeta values

Anomalous dimension of the simplest nontrivial single impurity operator in the beta=1/2 deformed theory is determined at six loops from the AdS/CFT correspondence. L\"uscher correction is evaluated at next-to-next-to-leading order (NNLO) in terms of multiple zeta values. The result can be simplified into the products of simple zeta functions and the same form of the correction is expected for the Konishi operator at six loops, too.Comment: 14 pages, references added, numeric coefficient in (5) corrected, which changes the numerical result, but not its structur

Recent Applications of Quantitative Structure-Activity Relationships in Drug Design

One of the most important challenges that face medicinal chemists today is the design of new drugs with improved properties and diminished side-effects for treating human disease such as AIDS and others. Medicinal chemists began the process by taking a lead structure and then finding analogs exhibiting the preferred biological activities. Next, they used their experience and chemical insight to eventually choose a nominee analog for further development. This process is difficult, expensive and took a long time. The conventional methods of drug discovery are now being supplemented by shortest approaches made possible by the accepting of the molecular processes involved in the original disease. In this view, the preliminary point in drug design is the molecular target which is receptor or enzyme in the body as an option of the existence of known lead structure

Discrete physical states and correction terms in the Supersymmetric c=1 Model of String Theory

In this work, we explore the example of supersymmetric c=1 model which is one of the simplest models of superstrings, with an elegant and transparent spectrum of physical states. We show how the presence of the ghost cohomologies enlarges the spectrum of states and leads to new intriguing symmetries of the theory and possibly leads to nontrivial relations of two-dimensional supergravity with physical theories in higher dimensions. We develop a general prescription for constructing the BRST-invariant and nontrivial vertex operators and we explicitly compute correction terms that restore BRST invariance of their corresponding currents.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:hep-th/0602209, arXiv:hep-th/0701044 by other author

Exploring QSARs for Inhibitory Activity of Non-peptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM

The support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of non-peptide HIV-1 protease inhibitors. Genetic algorithm (GA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R(2)) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q(2)) on SVM model was 0.9672, which proves the reliability of this model. The results suggest that TE2, Ui, GATS5e, Mor13e, ATS7m, Ss, Mor27e, and RDF035e are the main independent factors contributing to the inhibitory activities of the studied compounds.The authors would like to acknowledge the computational chemistry laboratory at Al-Quds University for providing Matlab software and for the time dedicated for performing the calculations of the study

Exploring Quantitative Structure-Activity Relationships (QSARs) of Non-Tri cyclic Cyclooxygenase-2 (COX-2) Inhibitors by MLR and PC-ANN

Quantitative structure–activity relationship study using principal component artificial neural network (PC-ANN) methodology was conducted to predict the inhibitory activities expressed as pIC50 of 73 non-tri cyclic cyclooxygenase-2 (COX-2) inhibitors. The results obtained by MLR shows that the best two models are close to each other with regression coefficient of 0.85. These optimal models were further analyzed by PC-ANN and the best model obtained was with regression coefficient of 0.823 for the test set. The lowest prediction sum of squares (PRESS) value obtained for the prediction set is 4.727 which accounts for predictability of the model. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models