Chemical spaces molecular similarity

Basak SC, Gute BD, Mills D, Hawkins DM. Quantitative molecular similarity methods in the property/toxicity estimation of chemicals a comparison of arbitrary versus tailored similarity spaces. J Mol Struct (Theochem) 2003 622 127-45. 

Two objects are similar and have similar properties to the extent that they have similar distributions of charge in real space. Thus chemical similarity should be defined and determined using the atoms of QTAIM whose properties are directly determined by their spatial charge distributions [32]. Current measures of molecular similarity are couched in terms of Carbo s molecular quantum similarity measure (MQSM) [33-35], a procedure that requires maximization of the spatial integration of the overlap of the density distributions of two molecules the similarity of which is to be determined, and where the product of the density distributions can be weighted by some operator [36]. The MQSM method has several difficulties associated with its implementation [31] ... 

The concepts of molecular similarity (1-3) and molecular diversity (4,5) play important roles in modern approaches to computer-aided molecular design. Molecular similarity provides the simplest, and most widely used, method for virtual screening and underlies the use of clustering methods on chemical databases. Molecular diversity analysis provides a range of tools for exploring the extent to which a set of molecules spans structural space, and underlies many approaches to compound selection and to the design of combinatorial libraries. Many different similarity and diversity methods have been described in the literature, and new methods continue to appear. This raises the question of how one can compare different methods, so as to identify the most appropriate method(s) for some particular application this chapter provides an overview of the ways in which this can be carried out, illustrating such comparisons by,... 

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. 

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. 

The quantification of molecular similarity generally involves three components molecular descriptors to characterize the molecules, weighting factors to differentiate more important characteristics from less important ones, and the similarity coefficient to quantify the degree of similarity between pairs of molecules (20, 21). The first two components are related to the definition of chemical space as discussed in Section 2.4. Therefore, it is natural to assume that structurally similar molecules should cluster together in a chemical space, and to define the similarity coefficient of a pair of molecules to be the distance between them in the chemical space. The shorter the distance is the more similar the pair is. 

Table 13.6 provides a comparison among several leading search methods in terms of their origin, search time, scope and nature of chemical space, format of input query ligand, and molecular similarity measure used. 

Godden JW, Furr JR, Xue L et al. (2004) Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality. J Chem Inf Comput Sci 44 21-29. 

A more profound visual difference of the chemical space covered by natural products and synthetic drugs was presented by Derek Tan, in which he applied a similar PCA analysis of 20 synthetic drugs (including ten best sellers of 2004) and 20 natural products.7 For this analysis, Tan used nine molecular descriptors—molecular weight, clog P, H-bond donors, H-bond acceptors, rotatable bonds, polar surface area (PSA), chiral centres, N and O atoms—and then applied PCA to reduce nine-dimensional vectors to two-dimensional vectors before re-plotting the data. 

D.L. Cooper, and N.L. Allan, Molecular Similarity and Momentum Space, R. Carbo, Ed. Molecular Similarity and Reactivity From Quantum Chemical to Phenomenological Approaches Kluwer Academic Publ. Dordrecht, The Netherlands, 1995, pp 31-55. 

The measurement of molecular diversity requires the definition of a chemical space. This A-dimensional chemical space is represented by a group of selected molecular descriptors. Each compound in a collection can be assigned coordinates based on the measurement of its descriptor values. Increasing distance, within the dimensions of the assigned coordinate space, should correlate with increasing diversity (or decreasing similarity) between compounds. 

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