Inappropriate spacing of the differentiating photoreceptors and moderate fusion of the ommatidial clusters

A pharmacophore with an excluded volume only matches if no atoms penetrate the excluded area. Catalyst-generated best pharmacophore model comprising of best selected chemical features were used as query for searching the chemical 3D databases. Virtual screening of such databases can serve two main purposes: first, validating the quality of the generated pharmacophore models by selective detection of compounds with known inhibitory activity, and, second, finding novel, potential leads suitable for further development. Thus, with the purpose of identifying novel lead compounds, the four-feature pharmacophore model obtained from HypoGen analysis was used as a three-dimensional query for database search. As a result of this search, 399 lead compounds were obtained from the 3D query and their activities were estimated, out of which 4 candidates emerged as potential ligands exhibiting a good perfect four feature fit. To explore the druggability of the molecules, ADME properties were checked by applying Lipinski’s rule on all the four compounds obtained from database screening. GSK2118436 Raf inhibitor Violation in number of HBD, HBA, molecular weight, and LogP were detected. As an additional validation setup, all the four identified lead compounds were mapped onto the structure-based pharmacophore. The mapping pattern was observed to augment the confidence in identified novel lead structures. The comparison of pharmacophoric features obtained from structure-based and ligand-based study revealed that both the pharmacophores have four common points i.e. two hydrogen bond acceptors and two hydrophobic groups. The pharmacophore obtained from structure-based study exhibited one additional feature i.e. hydrogen bond donors. This observation revealed that along with HBA and HY features, HBD feature can also contribute an additional interaction site at HIV-1 protease. All the 47 compounds of the compound library were mapped onto the generated structure-based pharmacophore. One of the interesting outcome of the study was that out of different conformations of 47 compounds, 351 hits were obtained and 41 hits exhibited a five-feature mapping and rest all showed a four-feature interaction. These hits presented the chemical features and the shape suggested by the structure-based pharmacophore model. Mapping fashion of least active compound 8t onto the structure-based pharmacophore was also analyzed, which exhibited a four-feature fit in which hydrogen bond acceptor was missing due to absence of cyclic urea carbonyl group and hence resulted in least biological activity. Interestingly, comparison of pharmacophoric interactions of both the pharmacophores display common binding mode and indicates the significance of hydrogen bond acceptor, donor and hydrophobic functionalities in defining the activities of compounds. It is also interesting to note that the seventeen different conformations of the compound 9s were obtained as hits, out of seventeen conformations sixteen mapped to four features of the input pharmacophore whereas one mapped to five features, i.e. two hydrogen bond acceptors, two hydrophobes and one hydrogen bond donor. It seems that one out of seventeen different conformers is able to adopt a orientation which can interact with all five pharmacophoric features at HIV-1 protease binding pocket due to conformational adjustment. Hence, the model developed herein also highlights the importance of bioactive conformation in eliciting the biological response. Also, 15 external test set molecules which were used to validate the pharmacophore developed from ligand-based methodology were also used as a screening validation dataset on the five-feature structure-based pharmacophore. All the 15 external test set molecules exhibited good estimated activities and fit values explaining the accuracy of our developed pharmacophore. The most active compounds of both non-cyclic and cyclic urea derivatives showed the best fit values. In order to validate the pharmacophoric pattern of above mentioned four database compounds, their conformations were generated and mapped onto the pharmacophore derived from structure-based strategy. Out of different conformations of four compounds, 19 hits were obtained and three hits exhibited a perfect five-feature mapping and rest all showed a four-feature interaction. Analysis of the best five feature hit exhibited by highest estimated compound BTB01434, revealed that two HBA and two HY features were mapped exactly on the same groups as that of mapping obtained onto the pharmacophore obtained from HypoGen study.

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