The human kinome [1] is a highly conserved target family composed of more than 500 different proteins. Kinases play fundamental roles in many intracellular pathways such as cytokinesis, cell proliferation, differentiation, and apoptosis, and are therefore implicated in various diseases. Currently there are significant efforts in therapeutic areas such as cancer and inflammation to identify novel kinase inhibitors. Random screening of a discovery compound library often results in a hit rate of 0.1 %, [2] whereas focused library screening could improve this rate to ! 1 %. Consequently, libraries focused toward kinases have become starting points in screening campaigns [3] and are complementary to conventional high-throughput screening (HTS) [4] of discovery libraries. Compound collections catered to target families [5] such as kinases present a unique opportunity to explore discrete chemical, biological, and property spaces. In contrast, HTS libraries are built to represent maximum diversity in the chemical and biological properties of compounds. Focused libraries are also appealing due to decreased synthesis, repository management, and screening costs. The present work describes a rapid computational process to select a collection of compounds targeted as kinase inhibitors, combining the advantages of 2D and 3D virtual screening methods for use in kinase-focused screening campaigns.In designing a general kinase screening library, the challenge lies in defining the chemical and biological space that identifies compounds with utility against any of the many possible kinase targets. Whereas some inhibitors such as staurosporine are known to be active against many kinases, others show a specific inhibitory profile. [6,7] The limited selectivity of many inhibitors is due to the fact that the catalytic ATP binding domain targeted is highly conserved. In recognition of the difficulty of designing selective kinase inhibitors, dual-and multitarget kinase inhibitors were developed.[8] Such "dirty drugs" (i.e., sorefenib, sunitinib) have gained much attention in recent years and show potential to be advantageous in cancer therapy. These clinical developments have also contributed to the increased interest in general kinase inhibitor libraries. [9] In this work, our goal was to develop a focused library selection procedure based on a 2D similarity search combined with 3D target-based filtering. A recent review argues that "2D fingerprints are surprisingly effective in many search situations in comparison with more complex 3D designs".[10] Indeed, 2D approaches allow a rapid analogue search from various databases.[11] We find the combination of 2D and 3D methods has distinct advantages; it can decrease the number of false negatives, and 2D methods can represent a pre-filtering tool that enables real-time 3D virtual screening using traditional docking algorithms tailored to the evaluation of large numbers of molecules. Herein we describe our methods and characterize the general kinase-focused library.

A novel diversity assessment method, the Explicit Diversity Index (EDI), is introduced for druglike molecules. EDI combines structural and synthesis-related dissimilarity values and expresses them as a single number. As an easily interpretable measure, it facilitates the decision making in the design of combinatorial libraries, and it might assist in the comparison of compound sets provided by different manufacturers. Because of its rapid calculation algorithm, EDI enables the diversity assessment of in-house or commercial compound collections.

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