oa Editorial [Hot topic: QSAR and Complex Networks in Pharmaceutical Design, Microbiology, Parasitology, Toxicology, Cancer and Neurosciences (Executive Editor: Humberto Gonzalez-Diaz)]

Both, computer-aided Pharmaceutical Design and Drug Target Discovery using Bioinformatics are valuable tools in biomedical sciences. They may become useful in order to reduce costs in terms of material resources, personal, time and the use of animals of laboratory in the exploration of large databases. These techniques are not aimed to replace experimentation at all; we should understand these methods only as a guide to “seek the needle in the haystack”. There are many computational techniques and mathematical models useful in this sense. In particular, Graph theory is of special interest due to its high flexibility to study many types of systems ranging from drug molecules to drug target proteins and beyond. In fact, many authors have used molecular graphs to represent the structure of drugs by means of vertices (represented by dots) that represent atoms and edges that represent chemical bonds. Consequently, molecular graphs express the structure of organic compounds in terms of atom connectivity. In addition, we can associate graphs with different classes of numeric matrices to carry out computational studies. The Boolean or Adjacency matrices are perhaps the more simple to explain. These matrices are square tables with elements bij = 1 for pair of connected nodes and 0 otherwise. At one higher structural level we can use essentially the same type of graphs to study complex networks used to represent the 3D structures of proteins (enzymes, molecular targets, channels, receptors). The construction of this type of graphs and matrices is straightforward to realize in an intuitive form taking into consideration the analogy between the previous situations. In these networks, aminoacids often play the role of nodes and links express spatial contact between two aminoacids (see also, contact maps or residue networks). In the same group with proteins we can find the graphs used to represent the secondary structure of RNAs. In this last class of networks, nucleotides often play the role of nodes and links express that a pair of bases are sequence neighbors or are involved in a hydrogen bond. In parallel, many authors have been used Graph and Complex Network theory to approach very large networks with low computational cost. These large networks are graphical representations of real bio-systems with essentially two components nodes and links in a broad sense. In the case of bio-systems of certain relevance for Current Pharmaceutical Design we can name drug-target networks, protein interaction networks (PINs) used to represent proteomes, drug-tissue action networks, and drug - disease/gen-disease networks for diseasome, to cite only some examples. These are the same type of above-mentioned graphs but nodes are not atoms or aminoacids but proteins, tissues, targets, patients, diseases, population groups, disease incidence regions, etc. Node-to-node links (edges or arcs) express different types of ties or relationships between two nodes as for instance: drug-target inhibition, gen-disease regulation. In all these cases, we can easily calculate different invariant parameters of the matrices associated to the graphs that may be used to describe the structure of these objects (drugs, proteins, or large bio-systems). As this numbers are based only on connectivity information they are often named as Connectivity measures or Topological Indices (TIs). To recommended readings connecting these topics are both the comprehensive handbook in graph and complex networks [1] and the handbook of molecular descriptors [2]. In fact, in our days, there is an explosion on the use of Topological Indices (TIs) of Graphs and Complex Networks on a broad spectrum of topics related to Drug metabolism and distribution research. Using TIs as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems in principle. We see QSPR model as a function that predict the properties of the system (drug, protein, RNA, diseasome) using parameters that numerically describe the structure of the system (like TIs). There are many QSPR-like terms that fit to more specific situations, for instance Quantitative Structure-Activity Relationships (QSAR), Quantitative Structure-Toxicity Relationships (QSPR), Quantitative Proteome-Property Relationships (QPPR), Quantitative Sequence-Action Model (QSAM), or Quantitative Structure-Reactivity Relationships (QSRR), to cite a few examples. In all this cases we can find models that use the TIs of the system as input to predict the properties of this system (output), see the recent book edited by Gonzalez-Diaz and Munteanu in 2010 [3]. In a recent, preliminary review in the field published in Proteomics in 2008 Gonzalez-Diaz et al. discussed the use of these methods but only from the point of view of proteins [4]. Next we extended the discussion to a collective of authors edited a special issue on TIs but ever restricted to the field of protein and proteomics; published in Current Proteomics in December 2009 [5-11]. In other recent issue, we guestedited [12] a series of papers devoted to QSPR techniques but only from the point of view of low-molecular-weight drugs without discussion of metabolism or distribution; this issue was published in Current Topics in Medicinal Chemistry in 2008 [12-21]. Last, we guest-edited [22] an issue focused on graph TIs approach to Drug ADMET processes and Metabolomics, see the papers published in the issue of may 2010 for the journal Current Drug Metabolism [23-30]. In any case, we believe that there is necessity of a collection of manuscripts or issue more focused on QSAR, TIs and networks applied to pharmaceutical design at all structural levels. Based on all these reasons we edited the present issue including QSPR/QSAR studies with applications to Pharmaceutical Design and related areas like Microbiology, Parasitology, Pharmacology, Chemoterapy, Epidemiology, Toxicology, and others.