Status of HTS Data Mining Approaches

High‐throughput screening of large compound collections results in large sets of data. This review gives an overview of the most frequently employed computational techniques for the analysis of such data and the establishment of first QSAR models. Various methods for descriptor selection, classification and data mining are discussed. Recent trends include the application of kernel‐based machine learning methods for the design of focused libraries and compilation of target‐family biased compound collections.