In this work, we reviewed different aspects about the epidemiology, drugs, targets, chem-bioinformatics, and
systems biology methods, related to AIDS/HIV. Next, we developed a new model to predict complex networks of the
prevalence of AIDS in U.S. counties taking into consideration the values of Gini coefficients of social income inequality.
We also used 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.
We obtained the data about AIDS prevalence and Gini coefficient from the AIDSVu database of Emory University and
the data about anti-HIV drugs from ChEMBL database. We used Box-Jenkins operators to measure the shift with respect
to average behavior of counties from states and drugs from reference compounds assayed in a given protocol, target, or
organism. 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 counties in U.S. 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.