Abstract
Activity landscapes (ALs) of compound data sets are rationalized as graphical representations that integrate similarity and potency relationships between active compounds. ALs enable the visualization of structure–activity relationship (SAR) information and are thus computational tools of interest for medicinal chemistry. For AL generation, similarity and potency relationships are typically evaluated in a pairwise manner and major AL features are assessed at the level of compound pairs. In this study, we add a conditional probability formalism to AL design that makes it possible to quantify the probability of individual compounds to contribute to characteristic AL features. Making this information graphically accessible in a molecular network-based AL representation is shown to further increase AL information content and helps to quickly focus on SAR-informative compound subsets. This feature probability-based AL variant extends the current spectrum of AL representations for medicinal chemistry applications.
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Acknowledgments
The authors thank Dr. Dilyana Dimova for providing activity landscape models using alternative similarity measures and Dr. Ye Hu for help with data sets. B.Z. is supported by the China Scholarship Council.
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Zhang, B., Vogt, M. & Bajorath, J. Design of an activity landscape view taking compound-based feature probabilities into account. J Comput Aided Mol Des 28, 919–926 (2014). https://doi.org/10.1007/s10822-014-9773-8
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DOI: https://doi.org/10.1007/s10822-014-9773-8