This study aims at developing a quantitative structure-property relationship (QSPR) model for predicting complexation with beta-cyclodextrins (beta-CD) based on a large variety of organic compounds. Molecular descriptors were computed following the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach and correlated with beta-CD complex stability constants by linear multivariate data analysis. This strategy afforded a final QSPR model that was able to explain around 86% of the variance in the experimental activity, along with showing good internal cross-validation statistics, and also good predictivity on external data. Topological substructural information influencing the complexation with beta-CD was extracted from the QSPR model. This revealed that the major driving forces for complexation are hydrophobicity and van der Waals interactions. Therefore, the presence of hydrophobic groups (hydrocarbon chains, aryl groups, etc.) and voluminous species (Cl, Br, I, etc.) in the molecules renders easy their complexity with beta-CDs. To our knowledge, this is the first time a correlation between TOPS-MODE descriptors and complexing abilities of beta-CDs has been reported. (C) 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci98:4557-4576, 2009

The aza-Diels-Alder reaction of cyclopentadiene with protonated (S)-phenylethylimine of methyl carboxilate was studied using density functional theory (DFT) at the B3LYP/6-31G(d) level to elucidate the reported stereoselectivity of this reaction. Four independent reaction pathways were found, all of them proceeding through a concerted, asynchronous, mechanism. Inclusion of solvent effects revealed a high exo/endo stereoselectivity that decreases with increasing temperature, in good accordance with the experimental reports. (c) 2009 Published by Elsevier B.V.

NO dissociation with and without the presence of hydrogen on the Au(321) surface was investigated using density functional theory and a periodic supercell approach. The role of hydrogen in the reaction of NO dissociation is studied by comparing four different routes (i.e. direct dissociation of NO on the clean surface and paths involving reaction with hydrogen adatoms prior to N-O bond cleavage). In the latter situation, two routes via a NOH intermediate were considered, producing N* + OH* or N* + H(2)O*, and another route via a NHOH intermediate was also considered, yielding NH* and OH* species. The calculations predict that the kinetically most favorable route is that producing nitrogen adatoms and water, i.e., NO* + 2H* -> N* + H(2)O* via the NOH* intermediate. This reaction is exothermic (1.1 eV) and the energy barriers for the separate NO* + H* -> NOH* and NOH* + H* -> N* + H(2)O* reactions are similar to 0.5 eV. An identical energy barrier was calculated for the reaction of the dissociation of molecular hydrogen on the stepped surface studied here, which is one-half of the barrier calculated in the case of the planar Au(111) surface. The reaction thermodynamically more favorable is that via the NHOH intermediate (1.5 eV). The kinetically least favorable path for NO dissociation on Au(321) is that occurring on the clean surface with an energy barrier of 3.5 eV; this reaction is also highly endothermic (> 2.2 eV). The present work shows that the presence of hydrogen is a necessary condition for NO dissociation on this stepped surface.

Cancer is among the top ten causes of death in the world but in spite of the efforts of the pharmaceutical companies and many governmental organizations, new and more effective drugs are urgently needed. Computer-assisted studies have been widely used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines, and datasets of congeneric and non-congeneric compounds. This paper describes a QSAR study and the successful application of 3D-MoRSE descriptor for developing Linear Discriminant Analysis (LDA) to predict the anticancer potential of a diverse set of indolocarbazoles derivatives. Despite the structural complexity of this sort of compounds the used variables are able to identify the most remarkable features like the incidence of polarizability of the substituents and the interatomic distance in the 7-azaindole moiety in the antiproliferative activity. A comparison with other approaches such as the Getaway, Randic molecular profile, Geometrical, RDF descriptors, was carried out showing the model with 3D-MoRSE descriptor resulting in the best accuracy and predictive capability. An LDA-based desirability analysis was conducted to select the levels of the predictor variables, in other words, the values of the independent variables which should generate more desirable anticancer chemicals, i.e., with higher posterior probability to be classified cytotoxic.

The conformational preferences of cocaine and ecgonine methyl ester were determined through ab initio and density functional theory calculations. They share the same preferred orientation of the acetate group with a hydrogen bond between the amine and carbonyl groups, and s-cis conformation for the methoxyl group. The benzoyloxy group of cocaine defines a specific accessible conformational region. In solution the most stable conformers are stabilized by internal hydrogen bonds in contrast to the lesser stables, which are stabilized by solute/solvent interactions. Overall, these conformational features explain why ecgonine methyl ester is the principal metabolite of cocaine in a human environment.

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity (I(i)) signals with m/z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCA-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activity/toxicity prediction have been described in the literature, using molecular 0D, 1D, 2D and 3D descriptors. Also these descriptors have been implemented in available computational tools such as DRAGON, SYBYL and CODESSA for it easy use. However, many of them only have been used to explain a few prediction problems. This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse. Several topological molecular descriptors applications are described, ranging from simple topological indices to topological indices derived from matrices weighted with atomic and bond properties. Their advantages, limitations and its possibilities in drug design are also discussed.

Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and phannacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis. (c) 2007 Wiley Periodicals, Inc.