Editorial [Hot Topic:Advances in Integration of Cheminformatics & Bioinformatics Special Issue (Editor: Petr Kocis)]

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.