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Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-ReactKIN, a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-ReactKIN relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (>0.5) sensitivity at the expense of a relatively low false positive rate (<0.5). Furthermore, in a case study, we demonstrate how X-React KIN can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at © 2010 American Chemical Society.

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Molecular Pharmaceutics

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