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 ? 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.
CITATION STYLE
Matta, C. E. D., Paiva, H. M., Galvão, R. K. H., Araújo, M. C. U., Soares, S. F. C., Weber, K. C., & Pinto, L. A. (2016). An active search method for finding objects with near-optimal property values within a given set. Journal of the Brazilian Chemical Society, 27(7), 1177–1187. https://doi.org/10.5935/0103-5053.20160014
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