The virtual screening approach for docking small molecules into a known protein structure is a powerful tool for drug design. In this work, a combined docking and neural network approach, using a selforganizing map, has been developed and applied to screen anti-HIV-1 inhibitors for two targets, HIV-1 RT and HIV-1 PR, from active compounds available in the Thai Medicinal Plants Database. Based on nevirapine and calanolide A as reference structures in the HIV-1 RT binding site and XK-263 in the HIV-1 PR binding site, 2,684 compounds in the database were docked into the target enzymes. Self-organizing maps were then generated with respect to three types of pharmacophoric groups. The map of the reference structures were then superimposed on the feature maps of all screened compounds. Only the structures having similar features to the reference compounds were accepted. By using the SOMs, the number of candidates for HIV-1 RT was reduced to six and nine compounds consistent with nevirapine and calanolide A, respectively, as references. For the HIV-1 PR target, there are 135 screened compounds showed good agreement with the XK-263 feature map. These screened compounds will be further tested for their HIV-1 inhibitory affinities. The obtained results indicate that this combined method is clearly helpful to perform the successive screening and to reduce the analyzing step from AutoDock and scoring procedure.
Keywords: hiv rt, hiv pr, docking, self-organizing map, medicinal plants, virtual screening, semi-empirical pm method
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