Medicinal chemistry QSAR methods

Kubinyi H. QSAR Hanch analysis and related approaches. In Mannhold R, Krogsgaard-Larsen P, Timmerman H, editors, Methods and principles in medicinal chemistry, Vol. 1. Weinheim VCH, 1993. 

This is already the 37th volume in our series on Methods and Principles in Medicinal Chemistry which started in 1993 with a volume on QSAR Hansch Analysis and Related Approaches, written by Hugo Kubinyi. An average release of roughly three volumes per year indicates the increasing appreciation of the series in the MedChem world. 1 want to express my sincere thanks to my editor friends Hugo Kubinyi and Gerd Folkers for their continuous and precious contributions to the steady development of our series. 

The final part is devoted to a survey of molecular properties of special interest to the medicinal chemist. The Theory of Atoms in Molecules by R. F.W. Bader et al., presented in Chapter 7, enables the quantitative use of chemical concepts, for example those of the functional group in organic chemistry or molecular similarity in medicinal chemistry, for prediction and understanding of chemical processes. This contribution also discusses possible applications of the theory to QSAR. Another important property that can be derived by use of QC calculations is the molecular electrostatic potential. J.S. Murray and P. Politzer describe the use of this property for description of noncovalent interactions between ligand and receptor, and the design of new compounds with specific features (Chapter 8). In Chapter 9, H.D. and M. Holtje describe the use of QC methods to parameterize force-field parameters, and applications to a pharmacophore search of enzyme inhibitors. The authors also show the use of QC methods for investigation of charge-transfer complexes. 

Keseru G.M. (2003) Prediction of hERG potassium channel affinity by traditional and hologram QSAR methods. Bioorganic el Medicinal Chemistry Letters, 13, 2773-2775. 

Kubinyi, H. Methods and principles in medicinal chemistry. In QSAR Hansch Analysis and Related Approaches, Mannhofd, R., Keogsgaaed-iaesen, P., and Timmeeman, H. (Eds.). VCH, Weinheim, New York, 1993. 

The criticisms in the previous paragraphs lead to a question If Hansch analysis is of such questionable value, then why has an entire chapter of this textbook been devoted to the subject Despite the fading utility of classical QSAR methods such as Hansch analysis, the logic behind Hansch analysis is invaluable to medicinal chemistry. Synthetic chemists in the pharmaceutical industry intuitively consider the ideas used to construct Hansch equations. Ideas such as electronics, sterics, and lipophilicity underlie traditional SAR approaches in the laboratory. Critical analysis of activity data and emphasis on seeking holes in R-group selection are also fundamental to successful SAR on a lead. Through the study of Hansch analysis, all these crucial ideas are presented in a rational framework that helps demonstrate their relevance. Just as importantly, Hansch analysis provides the foundation for the next generation of QSAR comparative molecular field analysis. 

Comparative molecular field analysis (CoMFA) is a modern, powerful extension of the classical QSAR methods that were developed in the 1960s.14 While Hansch analysis is simple to understand and fairly easy for any medicinal chemist to perform, CoMFA requires specialized software and an understanding of statistics. Since CoMFA is outside the experience of most synthetic chemists, pharmaceutical companies have dedicated computational chemistry groups to handle advanced QSAR tasks. 

Attempts to quantitatively relate chemical structure to biological action were first initiated in the 19th century, but it was not until the 1960s that Hansch and Fujita devised a method that successfully incorporated quantitative measurements into SAR determinations (see section 4.4). The technique is referred to as QSAR (quantitative structure-activity relationships). One of its most successful uses has been in the development in the 1970s of the antiulcer agents cimetidine and ranitidine. Both SARs and QSARs are important parts of the foundations of medicinal chemistry. 

Sjostrom, M. and Eriksson, L. Application of Statistical Experimental Design and PLS Modelling in QSAR. In QSAR Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. H. van de Waterbeemd. Verlag Chemie, Weinheim, Germany, 1995... 

We start this chapter with an analysis of methods to predict log P and aqueous solubility. In this context, we discuss the issue of applicability domain for QSAR models and the accuracy of prediction. Data available for simple physicochemical and ADME/T properties are compared by discussing the limitations of prediction of biological ADME/T properties. We restrict ourselves to several absorption and distribution properties, without discussing ME/T models. The interested reader is referred to the relevant sections in Comprehensive Medicinal Chemistry 7/(>1100 pages). 

For illustration, we shall consider here one of the nonlinear variable selection methods that adopts a k-Nearest Neighbor (kNN) principle to QSAR [kNN-QSAR (49)]. Formally, this method implements the active analog principle that lies in the foundation of the modern medicinal chemistry. The kNN-QSAR method employs multiple topological (2D) or topographical (3D) descriptors of chemical structures and predicts biological activity of any compound as the average activity of k most similar molecules. This method can be used to analyze the structure-activity relationships (SAR) of a large number of compounds where a nonlinear SAR may predominate. 

Good, A.C., Mason, J.S. and Pickett, S.D. (2000) Pharmacophore pattern application in virtual screening, library design and QSAR, in Methods and Principles in Medicinal Chemistry, Vol. 10 (eds H.J. Bohm and G. Schneider), John Wiley Sons, Inc., USA, pp. 131-159. 

In this chapter, we want to give a short overview on 3D QSAR methods and report how to use them optimally in the context of medicinal chemistry. We will focus primarily on CoMFA as it has been the most successful and widely used approach in this field. For more detailed information, the reader is referred to several reference books and... 

Sippl, W. 3D-QSAR using the GRID/GOLPE approach. In Methods and Principles in Medicinal Chemistry, Molecular Interaction Fields (Cruciani, G., Ed.), Vol. 27. Wiley-VCH Weinheim, 2006, pp. 145-170. 

Kubinyi, H. (1993) QSAR Hansch Analysis and Related Approaches. In Manhold, R, Krogs-gaard-Larsen, P, and Timmermann, H. (eds.) Methods and Principles in Medicinal Chemistry, vol. 1. VCH, Weinheim, pp. 21-36. 

The Hansch method, known as quantitative structure-activity relationships (QSAR), has evolved to embrace a variety of techniques. A glance at the recently published proceedings of the European QSAR Conference [1] shows how much of an impact the methods of pharmacophore discovery have on the computational aspects of medicinal chemistry. Indeed, looking up publications that cite various pharmacophore discovery methods papers, it is surprising to see that the total has rapidly accelerated in the past few years, demanding that a review such as this sort through hundreds of papers. 

H. Kubinyi, QSAR Hansch Analysis and Related Approaches in Methods and Principles in Medicinal Chemistry, Vol. 1, R. Mannhold, P. Krogsgaard-Larsen and H. Timmerman (eds.), VCH Publishers, Weinheim, 1993, Chapter 9.3. 

This book is a greatly extended version of a text being published as a chapter The Quantitative Analysis of Structure-Activity Relationships" in Burger s Medicinal Chemistry and Drug Discovery [1110]. Chemometric methods in QSAR (chapters 4.6 and 5.3) are discussed in more detail in Volume 2 of this series [1111]. 

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