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.