Virtual screening for anti-HIV-1 RT and anti-HIV-1 PR inhibitors from the Thai medicinal plants database: a combined docking with neural networks approach.
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 self-organizing 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.
Sangma
Chak
C
Cheminformatics Research Unit, Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand. fscicsm@ku.ac.th
Chuakheaw
Daungmanee
D
Jongkon
Nipa
N
Saenbandit
Kittipong
K
Nunrium
Peerapol
P
Uthayopas
Putchong
P
Hannongbua
Supa
S
D019380:Anti-HIV Agents / Q000737:chemistry / Q000494:pharmacology*; D001665:Binding Sites; D020650:Combinatorial Chemistry Techniques / Q000379:methods; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D017320:HIV Protease Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D054303:HIV Reverse Transcriptase / Q000037:antagonists & inhibitors*; D016571:Neural Networks, Computer; D010946:Plants, Medicinal / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D054303:HIV Reverse Transcriptase