A Statistical Learning Method for the Prediction of Anti‐HIV Activity Using Topological Indices Based on Steiner Eccentricity

The prediction of molecular activity plays an important role in drug discovery. Various approaches have been devised to predict anti‐HIV activity based on topological indices. This study proposes Steiner 3‐eccentric connectivity index and Steiner 3‐eccentric distance sum and applies them to predict anti‐HIV activity of a molecule. A support vector machine model is established for the prediction of the anti‐HIV activity over a dataset comprising 1795 compounds. Different dimensions of feature vectors over Wiener index, Randić index, graph energy, Steiner 3‐eccentric connectivity index, and Steiner 3‐eccentric distance sum are considered in our experiment. Cross‐validation shows that the Steiner 3‐eccentric connectivity index and Steiner 3‐eccentric distance sum could be used to predict the anti‐HIV activity. A combination of indices would earn a good performance than a single index. However, sometimes, more indices could not provide a better performance in prediction of anti‐HIV activity.