Different aspects about the epidemiology, drugs, targets, chem-bioinformatics, and systems biology methods, related to AIDS/HIV have been reviewed. Next, we developed a new model to predict complex networks of the AIDS prevalence in U.S. counties taking into consideration the Gini coefficient (income inequality) and activity/structure data of anti-HIV drugs in preclinical assays. First, we trained different Artificial Neural Networks (ANNs) using as input Markov and Symmetry information indices of social networks and of molecular graphs, respectively. We obtained the data about AIDS prevalence and Gini coefficient from the AIDSVu database of the Rollins School of Public Health at Emory University and the data about anti-HIV compounds from ChEMBL database. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2310 US counties vs. ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found was a Linear Neural Network (LNN) with Accuracy, Specificity, Sensitivity, and AUROC above 0.72-0.73 in training and external validation series. The new linear equation was shown to be useful to generate complex network maps of drug activity vs. AIDS/HIV epidemiology in U.S. at county level.