A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

Effect of polyol excess on the electrical property of vegetable-polyurethane film

Freestanding castor oil-based polyurethane (PU) film was obtained using spin-coating method. The effect of polyol content was analysed by means of thermally stimulated depolarisation current and AC dielectric measurements techniques. Two relaxation peaks were observed in the temperature range of -40 to 60 degrees C for PU with different polyol contents. The presence of polyol excess provides a shift to lower temperature of the a relaxation and the decrease in the activation energy of the transition in this region might be attributed to the plasticising effect of the polyol. The peak at higher temperature is due to the Maxwell-Wagner-Sillars relaxation, which also shifts in the low temperature direction as the polyol content is increased.

Axial ligand influence on geometries, charge distributions and electronic structures of iron tetraazamacrocycle [Fe((II))TIM(X)(Y)](2+) complexes assessed by Density Functional Theory

We employed the Density Functional Theory along with small basis sets, B3LYP/LANL2DZ, for the study of FeTIM complexes with different pairs of axial ligands (CO, H(2)O, NH(3), imidazole and CH(3)CN). These calculations did not result in relevant changes of molecular quantities as bond lengths, vibrational frequencies and electronic populations supporting any significant back-donation to the carbonyl or acetonitrile axial ligands. Moreover, a back-donation mechanism to the macrocycle cannot be used to explain the observed changes in molecular properties along these complexes with CO or CH(3)CN. This work also indicates that complexes with CO show smaller binding energies and are less stable than complexes with CH(3)CN. Further, the electronic band with the largest intensity in the visible region (or close to this region) is associated to the transition from an occupied 3d orbital on iron to an empty pi* orbital located at the macrocycle. The energy of this Metal-to-Ligand Charge Transfer (MLCT) transition shows a linear relation to the total charge of the macrocycle in these complexes as given by Mulliken or Natural Population Analysis (NPA) formalisms. Finally, the macrocycle total charge seems to be influenced by the field induced by the axial ligands. (C) 2011 Elsevier Ltd. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.