Prediction of polypharmacology can be useful at the level of binary putative target identification. Additional utility derives from estimation of the potency of an off-target effect. The latter goal is challenging: in the surprising cases of off-target effects, the off-target ligands will be dissimilar to on-target ligands being designed. Many QSAR methods have utility in making predictions within highly related chemical series, but cannot generally be fruitfully applied for off-target activity quantification due to limited domains of applicability. The Surflex-QMOD approach has been demonstrated to produce accurate and scaffold-independent predictions of binding affinity by constructing an interpretable physical model of a binding site based only on the structures and activities of ligands. Results will be presented establishing that QMOD-derived models produce accurate predictions in cases ranging from limited data of heterogeneous scaffolds to ample data containing related scaffolds. The potential for quantifying off-target effect potencies will also be examined.

Physical binding site modeling for quantitative prediction of biological activities

243rd National Meeting (2012)

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