HIV is a member of the genus Lentivirus, part of the family of
Retroviridae. Two types of HIV have been characterized: HIV-1 and HIV-2.
HIV-1 is the most common and pathogenic strain of the virus. Despite the
success of highly active antiretroviral therapy (HAART) in controlling
HIV infection and reducing HIV associated mortality, current drug
regimens are unable to completely eradicate HIV infection.
Bioinformatics methods based on complex networks and Gene Ontologies
(GOs) come to our rescue in predicting the possible targets for such
diseases and are very useful in this area. This work reviews some
bioinformatics concepts and previous studies related to HIV research
using Gene Ontologies (GO), complex networks, and related methods. Also,
we report new results mapping natural compounds on potential drug target
for HIV network using GOs. The network is statistically analyzed and
represented by the graphical interpretation to encounter the hub nodes
and their locally parsed neighbors, ligands multi-receptor docking and
the propensity of drug targets in hub nodes and related sub-networks.