Evaluation of the local anaesthetic activity of 3-aminobenzo[d]isothiazole derivatives using the rat sciatic nerve model.

On the basis of computer prediction of biological activity by PASS and toxicity by DEREK, the most promising 32-alkylaminoacyl derivatives of 3-aminobenzo[d]isothiazole were selected for possible local anaesthetic action. This action was evaluated using an in vitro preparation of the isolated sciatic nerve of the rat and compared with lidocaine which was used as a reference compound. QSAR studies showed that the polarizability, polarity and molecular shape of molecules have a positive influence on their local anaesthetic activity, while contributions of aromatic CH and singly bonded nitrogen are negative. Since the estimated PASS probabilities to find local anaesthetic activity in the most active compounds are less than 50%, these compounds may be considered to be possible NCEs.

QSPR model of Henry's law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach.

Six quantitative structure-property relationship (QSPR) models for a diverse set of experimental data of Henry's law constant (H) of organic chemicals under environmental condition (T=25 degrees C; water-air system) have been developed based on four different molecular descriptor sets. Three different models based on the descriptors of CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis), Tsar, and Dragon software and a model based on a combined descriptor set from these packages, and in addition from HYBOT software, have been established using the stepwise regression method. The combined descriptors set model gave the best results. Furthermore, a genetic algorithm was used for descriptor selection from a combined set of descriptors, and a radial basis function network was utilized to establish a model with a low root mean square error (RMSE). The results of this study were compared with the well-known bond contribution and group contribution methods. The group contribution method failed to predict Henry's law constant of 170 from all 940 compounds in the data-set. RMSEs of 0.693, 0.798, and 0.564 were achieved for bond contribution, group contribution and the best QSPR model of this study, respectively, based on logarithm of H. Analysis of different QSPR models showed that hydrogen bonding between the organic solute and water as a solvent has the greatest influence on this partitioning phenomenon.

QSPR correlation of melting point for drug compounds based on different sources of molecular descriptors.

Five linear QSPR models for melting points (MP) of drug-like compounds are developed based on three different packages for molecular descriptor generation and a combined set of all descriptors. A data set of 323 gaseous, liquid, and solid compounds was used for this study. Two models from the combined set of descriptors based on stepwise regression and genetic algorithm (GA) descriptor selection methods have acceptable prediction abilities. The statistical results of these models are r2 = 0.673 and root-mean-square error (RMSE) of 40.4 degrees C for stepwise regression-based quantitative structure-property relationships (QSPRs) and r2 = 0.660 and RMSE of 41.1 degrees C for GA-based QSPRs. Interpretation of descriptors of all models showed a strong correlation of hydrogen bonding and molecular complexity with melting points of drug-like compounds.