A Decision Tree Learning Approach for the Classification and Analysis of High-Throughput Screening Data

Abstract

High-throughput screening (HTS) of large libraries of compounds is applied by drug companies to pick up active molecules. Besides this “fishing” part, HTS can also play an important role as a source for structure-activity analysis, which relates physicochemical or structural features to biological activity. The latter aspect, although of great importance, has hardly been explored over the last years due to i) the size and complexity of the data sets, ii) the lack of, or rather ignorance about, suitable mathematical tools, iii) the quality of the biological data — biological activity is frequently described by just two (active — not active) or three (active — medium active — not active) categories — and iv) the lack of appropriate molecule descriptors.