Anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), was modeled with the aid of chemometric techniques. The activity of these compounds was estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques and compared with the previous works. The results obtained using the MLR method indicate that the anti-HIV activity of the HEPT derivatives depends on the reverse of standard shadow area on the YZ plane and the ratio of the partial charges of the most positive atom to the most negative atom of the molecule. The best computational neural network model was a fully-connected, feed-forward method with a 6-6-1 architecture. The mean-square error for the prediction set using this network was 0.372 compared with 0.780 obtained using the MLR technique. Comparison of the quality of the ANN of this work with different MLR models shows that ANN has a better predictive power.