The treatment of acquired immunodeficiency syndrome (AIDS) is a challenging medical problem. TIBO is a nonnucleoside reverse transcriptase inhibitor, which binds non-competitively to the hydrophobic pocket on the p66 subunit of RT enzyme. We used a dataset consisting of physicochemical properties and reverse transcriptase inhibitor activities of 88 set of 4,5,6,7-tetrahydro-y-imidazo-[4,5,1-jk][1,4]-x-benzodiazepin-2-(1h)one derivatives that are variously substituted by halogens, alkyl groups. The dataset was taken from the BIOBYTE database at (www.davidhoekman.com). The concentration of the compound leading to 50% effect has been measured and expressed as IC50. The logarithm of the inverse of this parameter has been used as biological end points (log 1/C) in the QSAR studies. The evolutionary neural network (ENN) is a new system for modeling multivariate data. The strengths of ENN’s are that they can extract insignificant predictors, choose the size of the hidden layers and nodes and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Reverse Transcriptase Inhibitors. We have found out that Evolutionary Neural networks are better predictor of activity values than Multiple linear regression and Multilayered Perceptrons. We have calculated the correlation coefficient of each of the methods where we have found ENNs are the best
