Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.
The possibility of the existence of single-wall carbon nanotubes (SWNT) in organic solvents, in the form of clusters containing a number of SWNTs, is discussed. A theory is developed based on a bundletmodel for clusters, which enables describing the distribution function of clusters by size. Comparison of the calculated values of solubility with experimental data would permit obtaining energetic parameters characterizing the interaction of an SWNT with its surrounding in a solid phase or solution. Fullerenes and SWNTs are unique objects, whose behavior in many physical situations is characterized by remarkable peculiarities. Peculiarities in solutions show up in that fullerenes and SWNTs represent the only soluble forms of carbon, what is primary related to the originality in the molecular structure of fullerenes and SWNTs. The fullerene molecule is a virtually uniform closed spherical or spheroidal surface, having no sharp ridges or dents. Similarly, an SWNT is a smooth cylindrical unit. Both structures give rise to the relatively weak interaction between the neighbouring molecules in a crystal, and promote effective interaction of the molecules with those of a solvent. Another peculiarity in solutions is related to their tendency to form clusters, consisting of a number of fullerene molecules or SWNTs. The energy of interaction of a fullerene molecule or SWNT with solvent molecules is proportional to the surface of the former molecule, and roughly independent of the relative orientation of solvent molecules. A unified treatment is proposed in the framework of the bundlet model of a cluster, in accordance with which the free energy of an SWNT involved in a cluster consists of two components: a volume one proportional to the number of molecules n in a cluster and a surface one proportional to n1/2.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.