Modelling of multi-wall CNT devices by self-consistent analysis of multichannel transport.

We present a generalization of the self-consistent analysis of carbon nanotube (CNT) field effect transistors (FETs) to the case of multi-wall/multi-band coherent carrier transport. The contribution to charge diffusion, due to different walls and sub-bands of a multi-wall (mw) CNT is shown to be non-negligible, especially for high applied external voltages and 'large' diameters. The transmission line formalism is used in order to solve the Schrödinger equation for carrier propagation, coupled to the Poisson equation describing the spatial voltage distribution throughout the device. We provide detailed numerical results for semiconducting mw-nanotubes of different diameters and lengths, such as current-voltage characteristics and frequency responses.

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for alpha2-adrenoceptors.

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for alpha1 and alpha2 adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (Ki) are available in the literature [Barbaro et al., J. Med. Chem., 44 (2001) 2118; Betti et al., J. Med. Chem., 45 (2002) 3603; Barbaro et al., Bioorg. Med. Chem., 10 (2002) 361; Betti et al., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the beta estimates - so-called bouncing betas. Those predictors that contained information relevant to the alpha2 response were identified on the basis of their pairwise linear correlations with affinity (-log Ki) for alpha2 adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to alpha2-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.

Synthesis, biological evaluation, and pharmacophore generation of new pyridazinone derivatives with affinity toward alpha(1)- and alpha(2)-adrenoceptors.

A series of new pyridazin-3(2H)-one derivatives (3 and 4) were evaluated for their in vitro affinity toward both alpha(1)- and alpha(2)-adrenoceptors by radioligand receptor binding assays. All target compounds showed good affinities for the alpha(1)-adrenoceptor, with K(i) values in the low nanomolar range. The polymethylene chain constituting the spacer between the furoylpiperazinyl pyridazinone and the arylpiperazine moiety was shown to influence the affinity and selectivity of these compounds. Particularly, a gradual increase in affinity was observed by lengthening the polymethylene chain up to a maximum of seven carbon atoms. In addition, compound 3k, characterized by a very interesting alpha(1)-AR affinity (1.9 nM), was also shown to be a highly selective alpha(1)-AR antagonist, the affinity ratio for alpha(2)- and alpha(1)-adrenoceptors being 274. To gain insight into the structural features required for alpha(1) antagonist activity, the pyridazinone derivatives were submitted to a pharmacophore generation procedure using the program Catalyst. The resulting pharmacophore model showed high correlation and predictive power. It also rationalized the relationships between structural properties and biological data of, and external to, the pyridazinone class.