Abstract:Apart from thiazole, benzimidazole, and tetrazole family, some of the piperazine analogs also show significant pharmacophoric activities. The synthesis of piperazine through intermediate 3 occurred via coupling of substituted benzenethiol with chloro-nitrobenzene. The nitro group of the isolated intermediate was reduced via an iron-acetic acid system. The aniline intermediate was cyclized with bis(2-chloroethyl)amine hydrochloride to obtain piperazine moiety. The synthesized substituted piperazine derivatives were screened for antibacterial and antifungal activities. The antibacterial activity was tested against Staphylococcus aureus, Streptomyces epidermidis, Pseudomonas aeruginosa and Escherichia coli, and antifungal activity was tested against Candida albicans, Aspergillus niger, Aspergillus flavus and Aspergillus fumigatus. As a result, many of the synthesized compounds showed significant antimicrobial and antifungal properties.

In the present work, 2D-and 3D-quantitative structure-activity relationship (QSAR) analysis has been employed for a diverse set of eighty-nine quinoxalinones to identify the pharmacophoric features with significant correlation with the aldose reductase inhibitory activity. Using genetic algorithm (GA) as a variable selection method, multivariate linear regression (MLR) models were derived using a pool of molecular descriptors. All the sixdescriptor based GA-MLR QSAR models are statistically robust with coefficient of determination (R 2 ) > 0.80 and cross-validated R 2 > 0.77. The derived GA-MLR models were thoroughly validated using internal and external and Yscrambling techniques. The CoMFA like model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, is highly robust with R 2 > 0.93 and cross-validated R 2 > 0.73. The established QSAR and CoMFA like models are proficient in identify key pharmacophoric features that govern the aldose reductase inhibitory activity of quinoxalinones.Keywords: Aldose Reductase Activity · Quinoxalinones · QSAR · CoMFA like model [a] V.

Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve this target, Hsp90 (heat shock protein 90), which is responsible for stabilization of many oncoproteins in cancer cells, is a promising target for developing an anti-cancer drug. The QSAR (Quantitative Structure–Activity Relationship) could be useful to identify crucial pharmacophoric features to develop a Hsp90 inhibitor. Therefore, in the present work, a larger dataset encompassing 1141 diverse compounds was used to develop a multi-linear QSAR model with a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The new developed six-parameter model satisfies the recommended values for a good number of validation parameters such as R2tr = 0.78, Q2LMO = 0.77, R2ex = 0.78, and CCCex = 0.88. The present analysis reveals that the Hsp90 inhibitory activity is correlated with different types of nitrogen atoms and other hidden structural features such as the presence of hydrophobic ring/aromatic carbon atoms within a specific distance from the center of mass of the molecule, etc. Thus, the model successfully identified a variety of reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with Hsp90.

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Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure–activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R2tr = 0.815, Q2LMO = 0.808, R2ex = 0.814, CCCex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups—especially the H-bond capable groups—must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.