Chemoinformatics and Computational Chemical Biology

Rajarshi Guha, Guha, Rajarshi

In this chapter we discuss the landscape view of structure-activity relationships (SARs). The motivation for such a view is that SARs come in a variety of forms, such as those where small changes in structure lead to small changes in activity or where small structural lead to significant changes in activity (also termed activity cliffs). Thus, an SAR dataset is viewed as a landscape comprised of smooth plains, rolling hills, and jagged gorges. We review the history of this view and early quantitative approaches that attempted to encode the landscape. We then discuss some recent developments that directly characterize structure-activity landscapes, in one case with the goal of highlighting activity cliffs while the other allows one to resolve different types of SAR that may be present in a dataset. We highlight some applications of these approaches, such as predictive model development and SAR elucidation, to SAR datasets obtained from the literature. Finally, we conclude with a summary of the landscape approach and why it provides an intuitive and rigorous alternative to standard views of structure-activity data.