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For general screening libraries, structural diversity descriptors and drug-likeness indicators still do not guarantee the in vivo bioavailability for the candidates, which is considered a major bottleneck in drug development. Early prediction of pharmacokinetics (log P, log D), metabolism, and toxicity makes it possible to deal with ADME (adsorption, distribution, metabolism, excretion) related diversity as an extension to the classical diversity concepts. It opens several new possibilities for optimization of a discovery library before doing any experimental screening. This new diversity concept is demonstrated on a subset of MeDiverse, which is a diverse collection of pharmacologically relevant compounds selected from our in-house library. From consideration of the ADME interface in living systems, virtual secondary libraries of metabolites and retrometabolites (prodrugs) can be generated. These additional libraries readily enhance both the structural and ADME related diversity. This new opportunity in library design can substantially improve the success rate for in vivo lead generation from in vitro hits.

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The authors describe an innovative approach for designing novel inhibitors. This approach effectively integrates the emerging chemogenomics concept of target-family-based drug discovery with bioanalogous design strategies, including privileged structures, molecular frameworks as well as bioisosteric and bioanalogous/isofunctional modifications. The authors applied this method in the design of selective inhibitors of matrix metalloproteases (MMPs), also referred to as matrixins, on the basis of a unique analysis of the ligand-target knowledge base, the 'matrixinome'. For this analysis, the authors created an annotated MMP database containing ∼ 300 inhibitors with their published activity profile. The ligand space was then arranged into a lead evolution tree, where the substructural transformations in each virtual step led to marked changes in the activity pattern. This allowed subtype-specific privileged fragments to be extracted as well as modifications, which improve activity and/or selectivity. Furthermore, the compounds with the preferred activity profile were correlated with sequence homology as well as binding site similarity within the target family, thereby leading to the identification of substructural modifications that turn non-selective, biohomologous structures into selective inhibitors. The matrixinomic application of the authors' approach, therefore, provides an example of how the combination of ligand space knowledge with sequence-related data can radically improve the outcome of the lead optimisation process to achieve higher selectivity within a given target family.

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.