Chemoinformatics molecular similarity

This chapter provides an overview of the mathematics that underlies many of the similarity measures used in chemoinformatics. Each similarity measure is made up of two key elements (1) A mathematical representation of the relevant molecular information and (2) some form of similarity index or coefficient that is compatible with the representation. The mathematical forms typically used are sets, graphs, vectors, and functions, and each is discussed at length in this chapter. 

Chemoinformatics Concepts, Methods, and Tools for Discovery begins with an elaborate theoretical discussion of the concept of molecular similarity by Maggiora Shanmugasundaram that is one of the origins and cornerstones of chemoinformatics as we understand it today. Chapter 2 by Willett follows up on this theme and extends the discussion to molecular diversity, a related... 

In this chapter, we will give a brief introduction to the basic concepts of chemoinformatics and their relevance to chemical library design. In Section 2, we will describe chemical representation, molecular data, and molecular data mining in computer we will introduce some of the chemoinformatics concepts such as molecular descriptors, chemical space, dimension reduction, similarity and diversity and we will review the most useful methods and applications of chemoinformatics, the quantitative structure-activity relationship (QSAR), the quantitative structure-property relationship (QSPR), multiobjective optimization, and virtual screening. In Section 3, we will outline some of the elements of library design and connect chemoinformatics tools, such as molecular similarity, molecular diversity, and multiple objective optimizations, with designing optimal libraries. Finally, we will put library design into perspective in Section 4. 

Maldonado, A.G., Doucet, J.P., Petitjean, M. and Fan, B.T. (2006) Molecular similarity and diversity in chemoinformatics from theory to applications. Mol. Divers., 10, 39. Gillet, V.J., Willett, P. and Bradshaw, J. (1997) The effectiveness of reactant pools for generating structurally-driven combinatorial libraries. f. Chem. Inf. Comput. Sci., 37, 731. 

Contents I. Introduction 34 II. Molecular Descriptors and Physicochemical Properties 36 III. Molecular Databases and Chemical Space 37 IV. Chemoinformatics in Food Chemistry 40 V. Examples of Molecular Similarity, Pharmacophore Modeling, Molecular Docking, and QSAR in Food or Food-Related Components 43 A. Molecular similarity 43 B. Pharmacophore model 47 C. QSAR and QSPR 48 D. Molecular docking 49 VI. Concluding Remarks and Perspectives 52 Acknowledgments 53 References 53... 

Peltason, L. and Bajorath, J. (2007). Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem. Biol. 14, 489 97. Peltason, L. and Bajorath, J. (2008). Molecular similarity analysis in virtual screening. In "Chemoinformatics Approaches to Virtual Screening", (A. Vamek and A. Tropsha, eds), pp. 120-147. RSC Publishing, Cambridge, UK. 

Maggiora GM, Shanmugasundaram V. Molecular similarity measures. In Bajorath J, editor. Chemoinformatics and Computational Chemical Biology. New York Humana Press/Springer 2011. p 39-100. 

Willett P. Evaluation of molecular similarity and molecular diversity using biological activity data. In Bajorath J, editor. Chemoinformatics Concepts, Methods, and Tools for Drug Discovery. Totowa Humana Press 2004. p 51-63. 

The book begins with chemoinformatics methodology and so it ends. To close the circle, in the final chapter (Chapter 15), Jose L. Medina-Franco of the Torrey Pines Institute for Molecular Studies and Gerald M. Maggiora of the University of Arizona describe foundations of molecular similarity analysis, one of the central themes in chemoinformatics. The evaluation and quantification of molecular similarity as an indicator of activity similarity is at the core of many chemoinformatics methods and an intensely investigated research topic to this date, conceptually linked to the design and navigation of chemical feature spaces. 

Natural products continue to be an important resource for the discovery of therapeutically relevant molecules. In addition, naturally occurring molecules present a rich source of chemical diversity and information. This makes them an interesting target for chemoinformatic investigations, despite the fact that they are sometimes too large or chanicaUy too complex for the analysis of structure-activity relationships, molecular similarity calculations, or other computational studies. Recently, efforts were made to statistically analyze collections of natural products, explore their chemical information content, and compare them to synthetic compounds. 

Medina-Franco JL, Maggiora GM (2014) Molecular similarity analysis. In Bajorath J (ed) Chemoinformatics in drug discovery concepts, methods, and tools for drug discovery, chapter 15. Wiley, New York... 

Willett P (2009) Similarity methods in chemoinformatics. Annu Rev Inf Sci Technol 43 3-71 Maggiora GM, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57 3186-3204... 

Maggiora GM, Shanmugasundaiam V (2011) Molecular similarity measures. In Bajorath J (ed) Chemoinformatics and computational chemical biology. Chapter 2. Humana, New York Baldi P, Benz RW, Hirschbeig DS, Swamidass SJ (2007) Lossless compression of chemical FPs using integer entropy codes improves storage and retrieval. J Chem Inf Model 47 2098 2109... 

Chemical descriptors are used widely in chemoinformatics research to map the chemical features of compounds into the domain of numerical and statistical analysis.Once molecular features are expressed numerically, or as enumerated factor sets (e.g., structural keys), the tools for numerical and statistical analysis can then be applied to analyze and compare molecular similarity or diversity of compound collections. 

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. 

The SOSA approach can be enhanced by virtual screening methods which use reference compound sets and molecular descriptors together with advanced chemoinformatics methods to compare and rank the similarity of considered candidate molecules. 

The group of Prof Peter Willett from the University of Sheffield summarizes in Chapter 6 new chemoinformatics methods for similarity-based virtual screening which based on known active compounds are useful for the identification of new ligands for targets related by conserved molecular recognition. 

This chapter provides a brief overview of chemoinformatics and its applications to chemical library design. It is meant to be a quick starter and to serve as an invitation to readers for more in-depth exploration of the field. The topics covered in this chapter are chemical representation, chemical data and data mining, molecular descriptors, chemical space and dimension reduction, quantitative structure-activity relationship, similarity, diversity, and multiobjective optimization. 

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