A series of propyloxyacetyloxy- and alkoxy-containing mixed tail triphenylene based discotic liquid crystals, abbreviated as TP(OCnH2n+1)3(OCOCH2OC3H7)3, n = 4 ~ 8, and hexa(propyloxyacetyloxy)triphenylene, TP(OCOCH2OPr)6 have been synthesized and their liquid crystalline properties have been investigated through differential scanning calorimetry (DSC) and polarizing optical microscopy (POM). These mixed tail triphenylene derivatives exhibit much more stable hexagonal columnar mesophases (Colh) and much wider mesophase temperature ranges than their hexaalkoxytriphenylene TP(OR)6 and hexaalkanoyloxytriphenylene TP(OCOR’)6 analogues. The asymmetrical compounds 2,6,11-trialkoxy-3,7,10-tri(2-propyloxyacetyloxy)triphenylenes with n = 5 ~ 8 possess higher clearing points and wider mesophase ranges than their symmetrical isomers 2,6,10-trialkoxy-3,7,11-tri(2-propyloxyacetyloxy)triphenylenes.

Four derivatives of 1,5,6,7-tetrahydro-4H-indazol-4-one have been synthesized and computational studies on the tautomeric forms at different levels, from semiempirical AM1, ab initio Hartree-Fock HF/6-31G* and HF/6-31G** to density functional calculations B3LYP/6-31G** were carried out. They allowed to establish the most stable form in all cases. The results are in agreement with the experimental data.

Addition reactions of SO2 to 1,3-butadiene and heteroderivatives were studied by performing density functional theory (DFT) calculations together with the 6311+G* basis set. Reactants, products, and transition states for each reaction were localized and the IRC connecting reactants and products was also obtained. Magnetic properties were evaluated along the reaction path to elucidate the characteristics of the reactions studied with respect to their aromaticity and pericyclic character. The addition can proceed by means of a cheletropic reaction giving a fivemembered cycle as a product or via a cycloaddition resulting on a sixmembered cycle. The results thus obtained indicate that for most reactions studied the energy barrier is smaller for the cycloaddition process, whereas the most stable product comes from cheletropic reaction. From the analysis of the magnetic properties along the reaction path, all reactions exhibit aromaticity enhancement near the transition state and, therefore, show pericyclic character.

The general belief is that quantitative structure-activity relationships (QSAR) techniques work only for small molecules and, proteins sequences or, more recently, DNA sequences. However, with non-branched graph for proteins and DNA sequences the QSAR often have to be based on powerful non-linear techniques such as support vector machines. In our opinion linear QSAR models based in RNA could be useful to assign biological activity when alignment techniques fail due to low sequence homology. The idea bases in the high level of branching for the RNA graph. This work introduces the so called Markov electrostatic potentials k?M as a new class of RNA 2D-structure descriptors. Subsequently, we validate these molecular descriptors solving a QSAR classification problem for mycobacterial promoter sequences (mps), which constitute a very low sequence homology problem. The model developed (mps = –4.664·0cM + 0.991·1cM – 2.432) was intended to predict whether a naturally occurring sequence is an mps or not on the basis of the calculated kcM value for the corresponding RNA secondary structure. The RNAQSAR approach recognises 115/135 mps (85.2%) and 100% of control sequences. Average predictability and robustness were greater than 95%. A previous non-linear model predicts mps with slightly higher accuracy (97%) but uses a very large parameter space for DNA sequences. Conversely, the kcM-based RNA-QSAR encodes more structural information and needs only two variables.

Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic linear indices to distinguish s-stereoisomers. In order to test the potential of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate’s s-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models shown an accuracy of 100% and 96.65% for the training set; and 88.88% and 100% in the external test set, respectively. Canonical regression analysis corroborated the statistical quality of these models(Rcan of 0.78 and of 0.77) and was also used to compute biology activity canonical scores for each compound. After that, the prediction of the s-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.982 and s = 0.157) and stochastic (R2 = 0.941 and s = 0.267) 3D-chiral linear indices were used. The predictive power was assessed by the leave-one-out cross-validation experiment, yielding values of q2 = 0.982 (scv = 0.186) and q2 = 0.90 (scv = 0.319), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.904) and stochastic (q2 = 0.88) 3D-chiral linear indices are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices provide a powerful alternative to 3D-QSAR.

A TOPological Substructural MOlecular DEsign (TOPS-MODE) was used to predict the flux across human skin permeability coefficient for heterogeneous set of compounds. The obtained model explained more than 84 % of data variance and shown the importance of the hydrogen bonding and the hydrophobicity to describe the property under study. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the human skin coefficient for studied compounds. The present approximation proved to be a good method to studying the permeability skin human coefficient for the heterogeneous compounds, which could be extended to other series of compounds.

The present work continues our series on the use of MARCH-INSIDE molecular descriptors [parts I and II: J. Mol. Mod. (2002) 8: 237-245 and (2003) 9: 395-407]. These descriptors encode information regarding to the distribution of electrons in the molecule based on a simple stochastic approach to the idea of electronegativity equalization (Sanderson’s principle). Here, 3D-MARCH-INSIDE molecular descriptors for 667 organic compounds are used as input for a Linear Discriminant Analysis. This 2.5D-QSAR model discriminates between antibacterial compounds and non-antibacterial ones with a 92.9 % of accuracy in training sets. On the other hand, the model classifies correctly 94.0 % of the compounds in test set. Additionally, the present QSAR performs similar-to-better than other methods reported elsewhere. Finally, the discovery of a novel compound illustrates the use of the method. This compound, 2-bromo-3-(furan-2-yl)-3-oxo-propionamide have MIC50 of 6.25 and 12.50 µg/mL against Ps. Aeruginosa ATCC 27853 and E. Coli ATCC 27853 respectively while ampicillim, amoxicillim, clindamycin, and metronidazole have, for instance, MIC50 values higher 250 µg/mL against E. Coli. Consequently, the present method may becomes a useful tool for the in silico discovery of antibacterials.

Most of present mathematical models for rational design and synthesis of new drugs consider just the molecular structure. In the present article we pretend extending the use of Markov Chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, it is developed a general Markov model that describes 19 different drugs side effects grouped in 8 affected biological systems for 178 drugs, being 270 cases finally. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations(18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations (31 out of 41)/(23 out of 31). Additionally a Back-Projection Analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physic interpretation of the models obtained. This article develops a model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction (Apk (j)) by the first time.