oa Editorial [Hot topic: Computational Techniques for Lead Discovery from Nature (Executive Editor: Judith M. Rollinger)]

Intelligent and rational drug discovery and design are of paramount importance in the field of drug development due to a constant need of innovative drugs in the battle of difficult to treat diseases and an increasing number of newly revealed targets. In the multidisciplinary processes of drug discovery and design we are currently facing two facts: (i) Statistics show that the myriad of structurally diverse natural compounds are the most favoured source of new drugs for clinical use [1]. In the last decades, natural products have received a substantial boost as sources of novel drug candidates. Most of the known natural compounds are secondary metabolites which provide living systems with their characteristic features mandatory for survival. They are inherently structurally very diverse. About 40% of the chemical scaffolds of published natural products are unique and have not been made by any chemist [2]. Their biosynthesis is controlled and selected by evolution potentially to interact with numerous macromolecular targets that may probably be relevant for the prosperity of the host organism. This so-called bioactivity of a natural compound is not only restricted to the evolutionary focused target, they are also an excellent source of validated substructures for the design of novel drug candidates. Thus it can be assumed that a large number of drug leads and hits are conserved in this inexhaustible natural pool of molecules pre-screened by evolution. Digging out and recognizing the respective drug leads are challenging tasks for industry and academia, for medicinal chemists, pharmacognosists and pharmacologists. (ii) Drug design and discovery have moved toward more rational concepts based on the increasing understanding of the molecular principles of protein-ligand interactions and the millions of published structural data on activities (hits and non-hits) - a highly valuable pool of information, which is all too often lying idle. Spurred on by economic interests, fundamental advances have been made in integrating various computational tools to accelerate the drug development process. The rationale and the quality of hit compounds can be increased by adequate virtual predictions of biological activities based on the understanding of the relationships between a molecule's structure and its biological activity using different data mining strategies, e.g. pharmacophore-based virtual screening, docking procedures, pattern recognition methods, artificial neural networks. The goal of applying such methods is to mine more or less large compound databases in silico and to select a limited number of candidates for experimental investigations. Additional drug-like filters and predicted ADME properties may help to reduce failures in later stages of drug development. Though the impact of natural products as drug candidates on the one hand, and the high potential of computer-assisted drug discovery and design on the other hand are known [3, 4], their combined benefit has barely been tasted. A sensible adaptation of computational strategies is to profit from the unique chemical and biological diversity associated with natural products [5-7]. In silico techniques, however, must not be used exclusively as activity-predicting tools, since the results provide merely an indication for a putative activity. It is only by the creation of interfaces between computational tools, experimental methods, and in depth know-how from different disciplines of natural product sciences and drug design technologies that a reasonable standard of success can be achieved. It seems to be a challenging, but worthwhile endeavour to skilfully exploit knowledge from all these fields, and to sift through the enormous wealth of wonderful molecules from Nature. The review articles included in this hot topic issue of Current Pharmaceutical Design summarize current computational technologies, approaches and applications to access the natural products' outstanding properties and bioactivities from different perspectives. In the first review [8], Hiss et al. provide a thorough insight into techniques and applicability of nature-inspired algorithms for drug design. In addition to application scenarios, the authors discuss the strengths and limitations of ‘natural computing’....