The age old paradigm of quantitative structure-activity relationship (QSAR) is the congenericity principle which states that similar structures usually have similar properties.  But these days a lot of large and structurally diverse data sets of chemicals with the same experimental data (dependent variable) are available.  Starting with the same classes of descriptors we extracted the two subsets of the most significant predictors for the formulation of QSARs for two sets of chemicals:  A homogeneous set of 95 amine mutagens and a diverse set of 508 structurally diverse mutagens.  The predictors included calculated topostructural (TS), topochemical   (TC), geometrical, and quantum chemical (QC) indices. Whereas for the homogeneous amines, a small group of descriptors were sufficient for QSAR development, for the 508 diverse set we needed a large and diverse set of indices for effective QSAR formulation.  This empirical study thus vindicates the DIVERSITY BEGETS DIVERSITY paradigm of QSAR.

One popular method for the representation and characterization of chemical structure is through the use of their computed mathematical descriptors.  Such descriptors, often called molecular descriptors, quantify different aspects of molecular structure, viz., size, shape, branching, cyclicity, bonding patterns, etc.   Applications of discrete mathematics in the development of molecular descriptors began in the middle of the twentieth century and the trend is going on in an unabated manner even today.  While in the 1970s only a few descriptors could be calculated, currently available software can calculate a large number of descriptors for molecules or biomolecules like DNA/ RNA, proteins.  When p molecular descriptors are calculated for n molecules, the data set can be viewed as n vectors in p dimensions, each chemical being represented as a point in R^p.   Because many of the descriptors are strongly correlated, the n points in R^p will lie on a subspace of dimension lower than p.  Methods like principal components analysis (PCA) can be used to characterize the intrinsic dimensionality of chemical spaces.  Since the early 1980s, Basak et al have carried out PCA of various congeneric and diverse data sets relevant to new drug discovery and predictive toxicology.  PCs derived from mathematical chemodescriptors have been used in the formulation of quantitative structure-activity relationships (QSARs), clustering of large combinatorial libraries as well as quantitative molecular similarity analysis (QMSA).  This presentation will review the results of PCA carried out by Basak and coworkers since the early 1980s to the present time in the characterization and visualization of chemical spaces with special reference to three data sets: 1) 1) A large and structurally diverse set of 3,692 chemicals which was a subset of the Toxic Substances Control Act Inventory of the United States Environmental Protection Agency (USEPA), and 2) A data set of 74 alkanes, and 3) A virtual library of 248,832 psoralen derivatives.

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Attempts have been made to formulate quantitative structure=activity relationships (QSARs) for the prediction of property/ bioactivity of chemicals from their experimental test data as well as properties that can be computed directly from molecular structure without the input of any other experimental property.  Because both in drug design and hazard assessment of chemical scenarios relevant experimental data for property/ bioactivity estimation are not available for the majority of candidate chemicals, QSARs based on computed molecular descriptors are emerging as methods of choice for property/ bioactivity estimation in many cases.  Numerical graph invariants or topological indices, viz., topostructural (TS) indices, topochemical (TC) indices, as well as three-dimensional (3-D) descriptors, and quantum chemical (QC) indices have been used for QSAR formulation based on computed descriptors.  In the 1990s,  Basak et al formulated the concept of hierarchical quantitative structure=activity relationships (HiQSAR) in which TS, TC, 3-D, and QC descriptors were used in a graduated manner, the more computationally demanding descriptors being used only if the simpler ones did not give acceptable QSAR models.  Our experience with a substantial number of HiQSARs for physical, pharmacological, and toxicological properties of different congeneric as well diverse sets chemicals indicate that the combinations of TS + TC descriptors are capable of giving good quality QSARs in most situations.  The addition of 3-D or QC descriptors make marginal or no improvement in model quality after the use of TS+ TC descriptors.  At this age of “big data screening and analysis” this is a good news because QSARs derived from the less expensive and practically useful TS+ TC combination can be effective tools in the screening of large chemical libraries.

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Shape memory polymers (SMPs) have attracted extensive attention from basic and fundamental research to industrial and practical applications because they have emerged as a cheap and efficient alternative to well-known metallic shape-memory alloys. Among them, shape memory polyurethanes (SMPUs) own different applications such as the textile finishing, adhesives, coatings, automotive, furniture, construction, and thermal insulation and footwear industries, due to it can be synthesized with different types of molecular architectures by manipulating their composition and choosing properly the chemical structure of their components.

In this work, the synthesis and characterization of shape memory polyurethane, based on two steps polymerization, is reported. The hard segment of SMPU was composed of diisocyanate and a chain extender. On the other hand, the soft segment was prepared by polyols with different molecular weights. Depending on the structure of the synthetized polyurethanes, the materials presented different properties. Thermal characterization was performed by means of Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA). Furthermore, mechanical properties and shape memory effect were also determined by Dynamic Mechanical Analysis (DMA) and Thermo-Mechanical Analysis (TMA).

In this work, the reliability of Dried Blood Spot (DBS) as a sampling technique for drug analysis was studied by Ultra High Performance Liquid Chromatography coupled to Photodiode-Array and Fluorescence Detection (UHPLC-PDA-FLUO). DBS microsampling, a technique based on placing a drop of blood in a cotton support that is allowed to air dry, has lately noticed an increase in use in bioanalysis. Even thought it offers several advantages compared to common blood sampling methods, it also shows some limitations for quantitative analysis due to the dependence on different factors. In this study, the influence of some of them (haematocrit, blood volume and sampling position) has been investigated, using amiloride, propranolol and valsartan drugs as model compounds. According to the results, it has been concluded that the sampling position and the haematocrit have influence in the accuracy and precision of the quantitative results, therefore limiting the use of this technique. On the other hand, dispersion of the analytes in the blood drop depends on their physicochemical properties which implies that the distribution of each analyte must be carefully studied during method development.

Gamma radiation process for modification of commercial polymers is a widely applied technique to promote new physical, chemical and mechanical properties. Gamma irradiation originates free radicals which can induce chain scission or crosslinking in the polymer backbone. The aim of this work is to research the structural, thermal and mechanical changes induced on a commercial polycyclooctene (PCO) when it is irradiated with a gamma source of ⁶⁰Co. After gamma irradiation, gel content was determined by Soxhlet extraction in cyclohexane, and thermal properties were evaluated before Soxhlet extraction by means of Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). Finally, the shape memory properties were evaluated both qualitatively and quantitatively, the last one by Thermo-Mechanical Analysis (TMA).

In recent years, the improvements in computer hardware and software have allowed the simulation of molecules with an increasing number of atoms. Unfortunately, the most accurate electronic structure methods based on N-particle wavefunctions (WFN) remain computationally too expensive to be applied to large systems. The most efficient method is undoubtedly the density functional theory (DFT). However, current implementations of DFT suffer from several problems, for instance, in the description of multi-reference systems. An alternative to both DFT and WFN methods lies in the development of a functional theory based on the one-particle reduced density matrix in its spectral expansion, known as natural orbital functional (NOF) theory. Several functionals of this type have been proposed, for which validation is necessary. A well-known tool for calibration, testing, and benchmarking of an approximate electronic structure method is the harmonium atom.

In the harmonium atom, the electron-nucleus potential is replaced by a harmonic confinement, but the electron-electron Coulomb interaction remains. By varying the strength of the harmonic potential, the correlation regime of this system can be tuned, making possible the transition from the weakly to the strongly correlated regime. Accordingly, the harmonium stands as an adequate system for studying the behavior of approximate NOFs, since it is possible to contrast them with their exact counterparts obtained from the analytic solution. In this presentation, the comparison between the quasi-exact and approximate electron-electron repulsion energy provided by eight known NOFs, in the singlet state of the four-electron harmonium atom with varying confinements, is analyzed in some detail. The present approach, which will appear soon in the Journal of Chemical Physics 143, not only reveals the failures of the functionals but also pinpoints the causes. In general, the functional PNOF6 shows the most consistent behavior, with decent accuracy, along all confinement regimes studied.

With the explosive growth of biological sequences in this century, medical science has been undergoing an unprecedented revolution, as indicated by, but not limited to, the following four aspects: (i) Post Biological Sequence Modification, (ii) Genome Analysis, (iii) Personalized Medicine,  (iv) Drug-Target Interactions within Cellular Networking.

In this mini review different methods for the preparation of base- and sugar-fluorinated nucleoside will be discussed and the use of different fluorinating agents will be briefly elaborated within the text.  Introduction of fluorine substituent into pyrimidine and purine nucleosides surely  lead to dramatic change in the over all chemical reactivity. In fact, there are many examples of the base- and sugar-fluorinated nucleosides that make a great impact on chemistry, biochemistry, and drug discovery.

PT-QSRR models are Quantitative Structure-Reactivity Relationship (QSRR) models based on Perturbation Theory (PT) that may be useful for multi-objective optimization in organic synthesis. In this communication, we summarize some of the more important results and conclusions obtained in our previous research / review paper about PT-QSRR models published in Curr. Top. Med. Chem., 2013, 13, (5), 1713-1741. I this previous work, firstly we reviewed general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. In this previous work, we developed a new QSPR-Perturbation theory model that classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature, temperature of addition, or time of reaction). The model also account for changes in chemical structure (connectivity structure and/or chirality patterns in substrate, product, electrophile agent, organolithium, and ligand of the asymmetric catalyst).