RESUMEN
A new approach is introduced for the construction of a predictive quantitative structure-activity relationship model in which only ligand-receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand-receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF-LM-ANN) method was optimized and then evaluated by the prediction of pEC50 for some of the azine derivatives as non-nucleoside reverse transcriptase inhibitors. RF-LM-ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF-LM-ANN model based on the MMDs alone.
Asunto(s)
ARN Polimerasas Dirigidas por ADN/antagonistas & inhibidores , Inhibidores Enzimáticos/química , Compuestos Heterocíclicos/química , Ligandos , Simulación del Acoplamiento Molecular , Redes Neurales de la Computación , Unión Proteica , Relación Estructura-Actividad CuantitativaRESUMEN
Reaction of sulfonium ylides (Me)(2)SCHC(O)C(6)H(4)R (R = H; m-NO(2); p-NO(2); p-OMe; p-Me and p-Br) with AgNO(3) in dichloromethane leads to various compounds. Single crystal X-ray diffraction analysis reveals that the adducts take 3 forms: (i) two-dimensional polymer, [AgNO(3)(Me(2)SCHC(O)C(6)H(5))](n) (1), with nitrate bridges in which each nitrate coordinates to three silver atoms through two oxygen atoms and two Me(2)SCHC(O)C(6)H(5) ligands coordinate to silver centers through carbon atoms; (ii) cationic binuclear, [Ag(Me(2)SCHC(O)C(6)H(4)-m-NO(2))(2)](2)(NO(3))(2)·2H(2)O (2), in which Me(2)SCHC(O)C(6)H(4)-m-NO(2) ligands simultaneously coordinate through both carbon and oxygen atoms with nitrate as a counter ion, and (iii) cationic mononuclear and anionic binuclear, [Ag(Me(2)SCHC(O)C(6)H(4)-p-NO(2))(2)](2)[{AgNO(3)(µ-NO(3)) (Me(2)SCHC(O)C(6)H(4)-p-NO(2))}(2)]·2CH(3)OH (3), in which nitrate groups act as bridging as well as terminal ligands, and Me(2)SCHC(O)C(6)H(4)-p-NO(2) ligands display C-coordination. Characterization of the obtained compounds was also performed by infrared, (1)H- and (13)C-NMR spectroscopy and analytical data indicated a 1 : 2 stoichiometry between the silver(I) nitrate and ylide p-OMe (4) and 1 : 1 for ylides p-Me (5) and p-Br (6). In addition, the antibacterial effects of DMSO-solutions of complexes 1-6 were evaluated by the agar disc diffusion method against three Gram positive and three Gram negative bacteria. All complexes displayed antibacterial activity against these bacteria, with high levels of inhibitory potency exhibited against the Gram negative species.