Abstract
This paper proposes an active search method aimed at finding objects with optimal or near-optimal 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.
Highlights
In many analytical applications, the problem consists of finding an object with optimal or near-optimal value for a y-property of interest, within a given pool of objects
The proposed active search method is initialized by selecting n0 objects on the basis of the x-vectors alone, i.e., without using any information concerning the corresponding y-values
The proposed active search method was applied to each of these subsets in order to find the compound with the largest pKi value in each subset
Summary
The problem consists of finding an object with optimal or near-optimal value for a y-property of interest, within a given pool of objects. The proposed active search method is initialized by selecting n0 objects on the basis of the x-vectors alone, i.e., without using any information concerning the corresponding y-values. Step 6: end The index set of the n0 selected objects is Proposed active search method
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