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Abstract

This article revisits a particular aspect of the molecular similarity principle—the Neighborhood Behavior (NB) concept. Earlier, the NB optimality criterion was introduced to select descriptor spaces, combining a given descriptor set and a similarity metric, which optimally comply with the similarity principle. Here, we focus on a “local” analysis based on the neighborhood of individual bioactive compounds. The defined NB-score measures similarity-based virtual screening success when using individual actives as queries. Systematic studies of local NB have been performed on a large combinatorial library of compounds with reported IC 50 values for five proteases, involving more than 140 descriptor/metric combinations of various fragment- and pharmacophore-based descriptors and different similarity metrics. Although, for each descriptor/metrics combination, the NB-score heavily depends on the query compound, on the average, 2D pharmacophore-based descriptors outperformed their 3D counterparts.

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Acknowledgments

We are grateful to Dr. L. Weber for providing the Ugi data set. Dr. Y. Tanrikulu is thanked for programming the LIQUID software.

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Correspondence to Dragos Horvath.

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Horvath, D., Koch, C., Schneider, G. et al. Local neighborhood behavior in a combinatorial library context. J Comput Aided Mol Des 25, 237–252 (2011). https://doi.org/10.1007/s10822-011-9416-2

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  • DOI: https://doi.org/10.1007/s10822-011-9416-2

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