Application of support vector machine-based ranking strategies to search for target-selective compounds

Support vector machine (SVM)-based selectivity searching has recently been introduced to identify compounds in virtual screening libraries that are not only active for a target protein, but also selective for this target over a closely related member of the same protein family. In simulated virtual screening calculations, SVM-based strategies termed preference ranking and one-versus-all ranking were successfully applied to rank a database and enrich high-ranking positions with selective compounds while removing nonselective molecules from high ranks. In contrast to the original SVM approach developed for binary classification, these strategies enable learning from more than two classes, considering that distinguishing between selective, promiscuously active, and inactive compounds gives rise to a three-class prediction problem. In this chapter, we describe the extension of the one-versus-all strategy to four training classes. Furthermore, we present an adaptation of the preference ranking strategy that leads to higher recall of selective compounds than previously investigated approaches and is applicable in situations where the removal of nonselective compounds from high-ranking positions is not required.