Protein kinases are critical components of signaling pathways and trigger various biological events. Several members of this superfamily are interesting targets for novel therapeutic approaches. All known eukaryotic protein kinases exhibit a conserved catalytic core domain with an adenosine 5'-triphosphate (ATP) binding site, which often is targeted in drug discovery programs. However, as ATP is common to kinases and other proteins, specific protein-ligand interactions are crucial prerequisites for valuable ATP site-directed ligands. In the present study, a set of 26 X-ray structures of eukaryotic protein kinases were classified into subfamilies with similar protein-ligand interactions in the ATP binding site using a chemometrical approach based on principal component analysis (PCA) and consensus PCA. This classification does not rely on protein sequence similarities, as descriptors are derived from three-dimensional (3D) binding site information only computed using GRID molecular interaction fields. The resulting classification, which we refer to as "target family landscape", lead to the identification of common binding pattern and specific interaction sites for particular kinase subfamilies. Moreover, those findings are in good agreement with experimental selectivity profiles for a series of 2,6,9-substituted purines as CDK inhibitors. Their interpretation in structural terms unveiled favorable substitutions toward selective CDK inhibitors and thus allowed for a rational design of specific ligands with minimized side effects. Additional 3D-quantitative structure-activity relationship (QSAR) analyses of a larger set of CDK-directed purines lead to the identification of essential structural requirements for affinity in this CDK ATP binding site. The combined interpretation of 3D-QSAR and the kinase target family landscape provides a consistent view to protein-ligand interactions, which are both favorable for affinity and selectivity in this important subfamily.

During the practice of combinatorial chemistry, it has been realized that molecular diversity is not the only essential feature in a synthetically feasible library. In addition, it is of utmost importance to enrich potential libraries with those molecules which could be converted to viable drug candidates. Given the enormous number of potentially synthesizable compounds, there is a need to design a subset of true "drug-like" compounds. In addition, a paradigm shift in drug discovery has resulted in the integration of pharmacokinetic and drug development activities into early stages of lead discovery. In particular, in silico filters are being developed and used to help identify and screen out compounds that are unlikely to become drugs. This paper highlights recent computational approaches towards the design of drug-like compound libraries, in particular, the prediction of drug-likeness in a more general sense as well as intestinal absorption through passive transport, the permeation of the blood-brain barrier and recent developments towards identification of potentially metabolically unstable molecules. Current computational tools for library design allow the incorporation of medicinal chemistry knowledge into library planning by a variety of methods, ranging from the use of privileged building blocks and simple counting of structural properties (e.g. number of hydrogen bonding partners) to relatively complex regression or neural network-based models to explain oral bioavailability and other pharmacokinetic properties by structural features. These tools are being incorporated more frequently into drug design according to the "rule-of-five" which refers to simple descriptors correlated to oral drug absorption. Combining experimental knowledge with effective computational filtering and prediction of various aspects of drug-likeness thus facilitates the rapid and cost-effective elimination of poor candidates prior to synthesis and helps focus attention on interesting molecules.

We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the alpha1a receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.

The solution structure of the tetracyclic lantibiotic mersacidin in methanol (CD3OH) has been determined by NMR followed by distance bound driven dynamics and subsequent restrained molecular dynamics simulations combined with an iterative relaxation matrix approach and alternatively by a simulated annealing protocol. The molecular dynamics simulations were performed with the AMBER program system and with the INSIGHT program package. The distance bound driven dynamics calculation was conducted using a modified version of the DISGEO II program. The interproton distance restraints were derived from jump symmetrized rotating-frame Overhauser enhancement and exchange (JS-ROESY) spectra, which yield optimum sensitivity for medium-sized molecules like mersacidin. The connectivities via the sulfide bridges were unambiguously confirmed by heteronuclear NMR techniques (heteronuclear single quantum coherence and heteronuclear multiple bond correlation methods). Due to the tetracyclic structure, mersacidin exhibits a rather rigid globular shape, which neither belongs to the duramycin nor to the nisin structure type lantibiotics. The resulting structures for the simulated annealing protocol of restrained and subsequent free molecular dynamics were compared and found to be very similar.