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Important Descriptors (important + descriptor)
Selected AbstractsAssessing the impact of mixing assumptions on the estimation of streamwater mean residence timeHYDROLOGICAL PROCESSES, Issue 12 2010Fabrizio Fenicia Abstract Catchment streamwater mean residence time (Tmr) is an important descriptor of hydrological systems, reflecting their storage and flow pathway properties. Tmr is typically inferred from the composition of stable water isotopes (oxygen-18 and deuterium) in observed rainfall and discharge. Currently, lumped parameter models based on convolution and sinewave functions are usually used for tracer simulation. These traditional models are based on simplistic assumptions that are often known to be unrealistic, in particular, steady flow conditions, linearity, complete mixing and others. However, the effect of these assumptions on Tmr estimation is seldom evaluated. In this article, we build a conceptual model that overcomes several assumptions made in traditional mixing models. Using data from the experimental Maimai catchment (New Zealand), we compare a complete-mixing (CM) model, where rainfall water is assumed to mix completely and instantaneously with the total catchment storage, with a partial-mixing (PM) model, where the tracer input is divided between an ,active' and a ,dead' storage compartment. We show that the inferred distribution of Tmr is strongly dependent on the treatment of mixing processes and flow pathways. The CM model returns estimates of Tmr that are well identifiable and are in general agreement with previous studies of the Maimai catchment. On the other hand, the PM model,motivated by a priori catchment insights,provides Tmr estimates that appear exceedingly large and highly uncertain. This suggests that water isotope composition measurements in rainfall and discharge alone may be insufficient for inferring Tmr. Given our model hypothesis, we also analysed the effect of different controls on Tmr. It was found that Tmr is controlled primarily by the storage properties of the catchment, rather than by the speed of streamflow response. This provides guidance on the type of information necessary to improve Tmr estimation. Copyright © 2010 John Wiley & Sons, Ltd. [source] QSAR modeling of photosynthesis-inhibiting nostoclide derivativesPEST MANAGEMENT SCIENCE (FORMERLY: PESTICIDE SCIENCE), Issue 2 2010Róbson Ricardo Teixeira Abstract BACKGROUND: A statistical model, built using the CODESSA software package, was developed to describe the relationship between the structure of nostoclide derivatives and their ability to interfere with the electron transport chain in the Hill reaction. RESULTS: A QSAR treatment was carried out on a series of compounds designed using the naturally occurring toxin nostoclides to correlate molecular descriptors with their in vitro biological activity (the ability to interfere with light-driven reduction of ferricyanide by isolated spinach chloroplast thylakoid membranes). The treatment using the CODESSA software package resulted in a three-parameter model with n = 19, R2 = 0.83, F = 23.8 and R2cv = 0.72. In the proposed model, the Image of Onsager Kirkwood solvation energy, which gives a measure of the polarity of a given compound, is the most important descriptor. The model was internally validated. CONCLUSIONS: The results obtained in this study indicate that polarity, as expressed by the dipole moment, is the most relevant molecular property determining efficiency of photosynthetic inhibitory activity. Copyright © 2009 Society of Chemical Industry [source] Are Mechanistic and Statistical QSAR Approaches Really Different?MOLECULAR INFORMATICS, Issue 6-7 2010MLR Studies on 158 Cycloalkyl-Pyranones Abstract Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. [source] Quantitative Structure,Activity Relationship Studies for the Binding Affinities of Imidazobenzodiazepines for the ,6 Benzodiazepine Receptor Isoform Utilizing Optimized Blockwise Variable Combination by Particle Swarm Optimization for Partial Least Squares ModelingMOLECULAR INFORMATICS, Issue 1 2007Leqian Hu Abstract Binding affinities of a series of substituted imidazobenzodiazepines for the ,6 Benzodiazepine Receptor (BzR) isoform are investigated by the Optimized Blockwise Variable Combination (OBVC) by Particle Swarm Optimization (PSO) based on Partial Least Squares (PLS) modeling. The QSAR analysis result showed that MolRef, AlogP, MRCM**-3, Rotatable bonds (Rotlbonds), Hydrogen Bond Acceptors (Hbond acceptor), five Jurs descriptors, two Shadow indices descriptors and principal moment of inertia are the most important descriptors among all the investigated descriptors. One can change the molar refractivity, the polar interactions between molecules, the shape of the molecules, the principal moments of inertia about the principal axes of a molecule, the hydrophobic character of the molecule, the number of Rotlbonds and Hbond acceptors of the compounds to adjust the binding affinities of imidazobenzodiazepine for the ,6 BzR isoform. The Quantitative Structure,Activity Relationship (QSAR) analysis result was also compared with MLR, PLS, and hierarchical PLS algorithms. It has been demonstrated that OBVC by PSO for PLS modeling shows satisfactory performance in the QSAR analysis. [source] Artemisinin Derivatives with Antimalarial Activity against Plasmodium falciparum Designed with the Aid of Quantum Chemical and Partial Least Squares MethodsMOLECULAR INFORMATICS, Issue 8 2003Abstract Artemisinin derivatives with antimalarial activity against Plasmodium falciparum resistant to mefloquine are designed with the aid of Quantum Chemical and Partial Least Squares Methods. The PLS model with three principal components explaining 89.55% of total variance, Q2=0.83 and R2=0.92 was obtained for 14/5 molecules in the training/external validation set. The most important descriptors for the design of the model were one level above the lowest unoccupied molecular orbital energy (LUMO+1), atomic charges in atoms C9 and C11 (Q9) and (Q11) respectively, the maximum number of hydrogen atoms that might make contact with heme (NH) and RDF030,m (a radial distribution function centered at 3.0,Ĺ interatomic distance and weighted by atomic masses). From a set of ten proposed artemisinin derivatives, a new compound (26), was predicted with antimalarial activity higher than the compounds reported in literature. Molecular graphics and modeling supported the PLS results and revealed heme-ligand and protein-ligand stereoelectronic relationships as important for antimalarial activity. The most active 26 and 29 in the prediction set possess substituents at C9 able to extend to hemoglobin exterior, what determines the high activity of these compounds. [source] Correlation between molecular structures and relative electrophoretic mobility in capillary electrophoresis: AlkylpyridinesCHINESE JOURNAL OF CHEMISTRY, Issue 10 2003Xiao-Jun Yao Abstract The quantitative relationship between relative electrophoretic mobility in capillary electrophoresis for a series of 31 closely related alkylpyridines and their molecular structures was studied by using CODESSA. According to the t -test on the results, we found that the three most important descriptors affecting the mobility are the relative number of rings (NR), Min e-n attraction for a C,-N bond (MEN) and average complementary information index (ACIC). With these structure descriptors a good three-parameter linear model was developed to correlate the mobility of these compounds with their structures. This model can not only correctly predict the migration behavior of these compounds, but also find the structural factors which are responsible for the migration behavior of these compounds, thus can help to explain the separation mechanism of these compounds. The method used in this work can also be extended to the mobility-structure relationship research of other compounds. [source] | ||||||||||