oa Editorial [Hot Topic: QSAR/QSPR Models as Enabling Technologies for Drug & Targets Discovery in: Medicinal Chemistry, Microbiology-Parasitology, Neurosciences, Bioinformatics, Proteomics and Other Biomedical Sciences (Guest Editor: Humberto Gonzalez Diaz)]

Some years ago we assembled one special issue (Curr. Topics in Medicinal Chemistry, 2008, Vol. 8, No. 18) focused on the topic: Quantitative Structure-Activity and Structure-Property Relationships (QSAR/QSPR) applied to Medicinal Chemistry. We refer to QSAR/QSPR model as any general function (not necessarily linear or simple) that links the structure of system with their external properties. In QSAR/QSPR the structure of the system is described using numerical parameters that play the role of inputs of the model. This previous issue paved the way for many colleagues worldwide that used these works as an important state-of-art collection on QSAR/QSPR modeling [1-10]. This issue also served as source of inspiration for their works about computational models applied to Medicinal Chemistry and related Bio-Medical Sciences. In fact, after this first issue more special issues appeared as a natural consequence, were many groups have explored different areas of applications of QSAR/QSPR models. In particular, we have edited different issues with review/research papers about the applications of QSAR/QSPR models combined with Chemoinformatics, Bioinformatics, and Complex Networks techniques. Some of these issues coming after our initial issue in Medicinal Chemistry in 2008 [1-10] are: Curr. Proteomics in 2009 [11-15], Curr. Drug Metabolism in 2009 [16-24], Curr. Pharm. Des., in 2010 [25-34], and Curr. Bioinformatics in 2011 [35-46]. Now, passed the 10th Anniversary of CTMC our Editor-In-Chief Prof. Allen Reitz proposed this new issue. The idea is to look back and review the past and recent tendencies on QSAR/QSPR modeling. It may become an excellent opportunity to re-think about the future trends in the development of these methods. In so doing, we should re-think QSAR/QSPR modeling technique called to become one of the more important Enabling Technologies complementary to experimental techniques in the process of drug and target discovery. In fact, all the above-mentioned issues as well as in many other works published in recent years continued the development of various strategies to characterize and classify structural patterns of low weighted drugs by means of molecular descriptors useful as inputs in QSAR/QSPR modeling. It has become possible not only to continue the efforts to assess diversity or similarity within structure databases, but molecular descriptors also facilitate the identification of potential bioactive molecules from the rapidly increasing number of compound libraries. They even allow for a controlled de-novo design of new lead structures. The number and apparent diversity of molecular descriptors developed in this sense is wide covering from constitutional and physicochemical properties to 3D descriptors. For instance, many of the works published in the previous issues are based on indices collected in the Handbook of Molecular Descriptors (HMD) published by Todeschini & Consoni describes more than 6 000 molecular descriptors grouped on more than 15 different families [47]. Anyhow, the research in this field is far from ended and more recently we have seen and explosion on the use of the class of parameters called Topological Indices (TIs). TIs are parameters used describe numerically the structure of system represented by means of a graph. In these graphs we have essentially two classes of objects nodes (parts of the system) and edges (relationships between the parts of the system). In the classic Medicinal Chemistry context use to represent molecules by graphs were the atoms are represented by nodes and the chemical bonds by means of edges. Some TIs, previously collected in HMD, has called the attention of many researchers due to their important capacity to capture biologically relevant information on many different systems (including single molecules and more large systems) but being very simple and fast to calculate. In this sense, TI-like new descriptors appeared that come to reinforce the pool of indices published in HMD. In addition, in clear advantage with respect to other indices, TIs are enabling QSAR/QSPR analysis going beyond classic frontiers and opening new possibilities such as the study of proteins, RNAs, Complex Networks represent a plethora of complex bio-systems [1-47]. In this sense is that we stated here that QSAR/QSPR analysis based on classic parameters and helped by TIs-like indices is called to become an important enabling technology. That is way, the present collection of papers pretend to humbly call the attention of experimental and theoretical authors (of different and somehow parallel areas) on the new scenarios that may be considered when we put in the same bag all together Medicinal Chemistry, Drug Design, Proteomics, QSAR, Complex Systems theory, and Bioinformatics. The present issue aims to reach this goal taking as basis all the experience accumulated in these years. As a Guest-Editor of this special issue and also as Section editor for Enabling Technologies I would like to express my sincere appreciation to the contributing authors for their prompt submission of their manuscripts for this issue. Then, we hope that this issue will not only offer useful and interesting information to scientists who are involved in the field, but, perhaps more importantly, will also serve as an inspiration for new researchers.