An Active Search Method for Finding Objects with Near-Optimal Property Values within a Given Set

Abstract

This paper proposes an active search method aimed at finding objects with optimal or nearoptimal y-property values, on the basis of x-variables obtained by indirect, less costly methods. The proposed method progresses in a sequential manner, starting from a small subset of objects with known y-values. At each iteration, the K-nearest neighbour regression technique is employed to obtain estimates ŷ for the objects with unknown y-values. The object with best ŷ value is then subjected to a direct analysis procedure for evaluation of the y-property. Examples are presented with simulated data, as well as actual quantitative structure-activity relationship (QSAR) and near-infrared (NIR) spectrometry datasets. The QSAR and NIR case studies involve the search for maximal antidepressant activity in a set of arylpiperazine compounds and maximal pulp yield in a set of eucalyptus wood samples, respectively. In all these cases, the active search yielded results closer to the maximal y-value compared to the classical Kennard-Stone algorithm for object selection.