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During the last decade the number of generated data in drug discovery research has grown exponentially. Scientific data are very important, however, what is even more important are interrelationships among the data. This is the main theme of the special issue of Current Drug Discovery Technologies dedicated to Advances in Integration of Chemoinformatics & Bioinformatics. There has been an impressive technological progress in the area of chemo- and bioinformatics. However, from the perspective of complex processes in vivo, we still face challenges of proper target-compound data integration from disease to chemistry, as discussed by Steven Potts and colleagues. Margaret Haldeman et al. emphasize that Chemical Abstracts and SciFinder are no longer seen as an exclusively chemical source of information, but as an indispensable information source covering multidisciplinary data, e.g. protein sequences complementing chemical, synthetic and patent aspects of the corresponding protein- ligand interaction. In Hiromi Sato's paper a novel approach to understanding complex biological networks is discussed, including drug-disease information, signal transduction, and metabolic pathways as well as transcriptional regulations. Molecular recognition aspects of ligand-protein interaction, which was historically one of the main foci of early stages of drug design, is now being complemented by ADME and toxicology predictions, trying to address the fate of a drug in the organism. Konstantin Balakin with colleagues have applied Sammon non-linear maps, Support Vector Machines and Kohonen Self Organizing Maps to model various ADME properties including human intestinal absorption, blood brain barrier permeability, P450 binding and other properties. Joseph Contrera and colleagues present a discriminant QSAR analysis model for carcinogenic risk exemplified on more than 1000 compounds from the FDA Center for Drug Evaluation and Research Rodent Carcinogenicity database representing a two-year rat and mouse study. In their study more than 3000 analysis models were built leading to the best model which included 53 variables. Finally, in A.K. Madan's study a distance-based and other topological descriptors were used for the prediction of dopamine receptors binding affinities of arylpiperazine derivatives. A better understanding of these multidimensional problems and their conceptual integration followed by technological integration will allow us to be better equipped for designing new molecules that could eventually become medicines in their final stage of development, as opposed to just hits, leads, or discontinued clinical candidates.

The inaugural issue of Current Drug Discovery Technologies (CDDT) A multidimensional complexity of drug discovery is challenging both scientifically and technologically. Over the last decade, there has been a tremendous progress in our understanding of many drug discovery related scientific disciplines from medicinally relevant chemistry space to pathophysiology of disease state. However, we still face significant challenges both at a scientific as well as at a technological level. In this new journal it is our wish to reflect the complexity of this drug hunting effort but also offer scientific communications on advances to solve problems. Current Drug Discovery Technologies publishes top scientific papers and reviews that provide solutions and clarifications of the complex task of drug discovery and development. This inaugural issue of CDDT touches on several relevant themes from miniaturization in HTS to maximum recommended dose in clinical trials. As medicinal chemists design and develop biologically active molecules, applying synthetic, computational, and modeling approaches, ultimately, they are seeking the properly balanced function of a molecule. Paul Wender and colleagues formalize this function oriented synthesis in their paper exemplifying the concept on the development of bryostatin analog inhibiting cancer-cell growth at a subnanomolar level. Mark Velleca and his team review chemical genetic approaches that provide pharmacologically relevant target validation. The above-mentioned computational approaches are featured in Claussen's molecular docking and Blower's chemoinformatics articles. Paul Blower features his American Statistical Association 2003 Chemistry Award winning approach, combining recursive partitioning and simulated annealing, in his paper describing one of the ways to deal with large data sets e.g. from high throughput screening. The process of generating such large HTS data sets is in turn described in Sewing's paper, where the author and his colleagues reveal industrial experience with miniaturization of the HTS process at Pfizer. David Taft describes renal drug disposition, study of renal drug metabolism, and screening for clinically significant drug interactions using the isolated perfused rat kidney model. The challenge of a low throughput of ion channel assays is addressed in Niels Fertig's automated nano-patch-clamp technology paper. An estimation of maximum recommended therapeutic dose (MRTD) and no effect levels (NOEL) of compounds based on clinical trials is the theme of the FDA scientists Ed Matthews and colleagues. A set of challenging concepts? And what about drugs against drugs? Kim Janda and his colleagues review immunopharmacotherapy against drugs of abuse as an alternative to presently used medications for treatment of dependence disorders.