Preface [Hot topic: Molecular Descriptors in Medicinal Chemistry: From Design to QSAR via Internet-Based Resources (Executive Editor: Giulia Caron)]

The concept of Quantitative Structure-Activity Relationship (QSAR) has been widely revisited and extended in recent years. The original dependent variable (A) represented the desired therapeutic effect of the system when exposed to a bioactive compound. Today A can be replaced by P (property, e.g. lipophilicity), D (disposition, e.g. permeation), Pk (pharmacokinetics, e.g. distribution volume) or T (toxicology, e.g. LD50). It is therefore more appropriate (as Prof. Kubinyi has suggested) to see QSAR as a subset of the more general term SPC (Structure-Property Correlations), which covers all statistical mathematical methods used to correlate any molecular property (intrinsic, chemical or biological) to any other property, using statistical regression or pattern recognition techniques. However, despite the evolution of the original significance of A, activity remains the field of biologists, biochemists and pharmacologists and not of medicinal chemists. On the contrary, the latter are used to working with molecular descriptors, which can be defined as “numbers extracted by a well defined algorithm from a molecular representation of a complex system, i.e. the molecule” (Prof. Todeschini at http: / / www.disat.unimib.it / chm / Dragon.htm). Over the past two decades the increased demand for in silico tools to model new biological targets, combined with the development of informatics, has led to a very rapid increase in the number of molecular descriptors available. This issue brings together a number of papers that illustrate examples of the design, development, understanding and application of some widely-used molecular descriptors of pharmaceutical interest. In the first paper Prof. Gasteiger discusses the fundamental prerequisite for the determination of molecular descriptors: the various levels of structure representation. He then introduces his own theoretical approach to obtain descriptors. In the second paper, Dr. Petrauskas and his co-workers present alternative ways to generate descriptors and describe the potential of fragmental methods to obtain molecular determinants from experimental data, and their use in many areas of medicinal chemistry. Dr. Tetko then illustrates the use of Internet to access chemical information and software packages. In particular he discusses the Internet Java-based technology, which makes traditional software available worldwide. The logical consequence of this technological improvement is the ease with which a large database of compounds can now be submitted for calculation, in a few seconds obtaining an impressive spreadsheet of descriptors. Once obtained, descriptors should be used in the most productive way. By definition, molecular descriptors are a source of chemical information. The comparison between calculated and experimental data (if available, of course) besides validating the calculations, can provide information derived from their differences. In the fourth paper we describe this new way of obtaining structural information in the case of descriptors related to lipophilicity. Dr. Migliavacca then explains how chemometric techniques are able to handle a plethora of data and reduce redundant tables to a limited series of useful numbers and / or plots. As evidenced in the first paper, the calculation of molecular descriptors requires a clear representation of molecular structure, which is far from obvious in the case of metallo-organic compounds. In the sixth paper Dr. Maiocchi discusses the use of modeling techniques in designing gadolinium(III)-based contrast agents, the most widely used compounds in Magnetic Resonance Imaging (MRI). ADME prediction (Absorption, Distribution, Metabolism and Excretion) is one of the today's biggest challenges for medicinal chemists, and thus great efforts have been made in the application of computational technology to this field. In the last paper Dr. Lombardo and his co-workers review the use of molecular descriptors to determine in silico ADME properties for potential leads. I am very grateful to all contributors for the enthusiasm they have shown in accepting my invitation and in preparing their papers but I also would like to apologize to many other important scientists working in the field for my having been unable to contact all of them. I finally thank my colleague and friend Sonja Visentin for encouraging me to accept this exciting task.