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.

Synthesis and characterization of mono and polymeric cyclopalladated compounds: Crystal and molecular structure of [{Pd(dmba)(ONO(2))}(2)(mu-pz)] center dot H(2)O (pz = pyrazine)

In the treatment of cyclometallated dimer [Pd(dmba)(mu-Cl)](2) (dmba = N,N-dimethylbenzylamine) with AgNO(3) and acetonitrile the result was the monomeric cationic precursor [Pd(dmba)(NCMe)(2)](NO(3)) (NCMe=acetonitrile) (1). Compound 1 reacted with m-nitroaniline (m-NAN) and pirazine (pz), originating [Pd(dmba)(ONO(2))(m-NAN)] (2) and [{Pd(dmba)(ONO(2))}(2)(mu-pz)] center dot H(2)O (3), respectively. These compounds were characterized by elemental analysis, IR and NMR spectroscopy. The IR spectra of (2-3) display typical bands of monodentade O-bonded nitrate groups, whereas the NMR data of 3 are consistent with the presence of bridging pyrazine ligands. The structure of compound 3 was determined by Xray diffraction analysis. This packing consists of a supramolecular chain formed by hydrogen bonding between the water molecule and nitrato ligands of two consecutive [Pd(2)(dmba)(2)(ONO(2))2(mu-pz)] units. (c) 2008 Elsevier Ltd. All rights reserved.

Docking and molecular dynamics simulation of quinone compounds with trypanocidal activity

In this work, two different docking programs were used, AutoDock and FlexX, which use different types of scoring functions and searching methods. The docking poses of all quinone compounds studied stayed in the same region in the trypanothione reductase. This region is a hydrophobic pocket near to Phe396, Pro398 and Leu399 amino acid residues. The compounds studied displays a higher affinity in trypanothione reductase (TR) than glutathione reductase (GR), since only two out of 28 quinone compounds presented more favorable docking energy in the site of human enzyme. The interaction of quinone compounds with the TR enzyme is in agreement with other studies, which showed different binding sites from the ones formed by cysteines 52 and 58. To verify the results obtained by docking, we carried out a molecular dynamics simulation with the compounds that presented the highest and lowest docking energies. The results showed that the root mean square deviation (RMSD) between the initial and final pose were very small. In addition, the hydrogen bond pattern was conserved along the simulation. In the parasite enzyme, the amino acid residues Leu399, Met400 and Lys402 are replaced in the human enzyme by Met406, Tyr407 and Ala409, respectively. In view of the fact that Leu399 is an amino acid of the Z site, this difference could be explored to design selective inhibitors of TR.