Understanding structure-activity relationships(SAR) of a set of bioactive compounds is key to medicinal chemistry design. Computational techniques like statistical, pharmacophore and structure-based modeling can provide insights into SAR, but can also be misled by false assumptions. For example, one common assumption is that a series of similar compounds has a common binding mode or mechanism of action. Other assumptions include additivity of SAR from systematic changes, and the similarity principle: �similar molecules have similar biological effect�. Characterizing activity landscapes and early detection of activity cliffs are crucial to understanding global and local SAR characteristics, critical for ligand-based virtual screening or lead-optimization campaigns. Further, in data-sets with a wealth of historical information, visual examination of SAR could provide novel insights and reveal new directions. We adapted Network-like similarity graphs(NSGs, Bajorath and co-workers) to mine a Pfizer antibacterial data-set, and compare and contrast NSG-based visual data-mining results with a few traditional approaches.

Characterizing activity landscapes using network-like similarity graphs to mine antibacterial data

245th National Meeting (2013)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.