Chemical space representation

The table shows a number of representative descriptor types (there are many more) that can be used to define chemical spaces. Each descriptor adds a dimension (with discrete or continuous value ranges) to the chemical space representation (e.g., selection of 18 descriptors defines an 18-dimensional space). Axes of chemical space are orthogonal only if the applied molecular descriptors are uncorrelated (which is, in practice, hardly ever the case). 

Figure 1.19. Chemical space representation displaying diverse and focused (boxed) compound sets...

In systematic SAR analysis, molecular structure and similarity need to be represented and related to each other in a measurable form. Just like any molecular similarity approach, SAR analysis critically depends on molecular representations and the way similarity is measured. The nature of the chemical space representation determines the positions of the molecules in space and thus ultimately the shape of the activity landscape. Hence, SARs may differ considerably when changing chemical space and molecular representations. In this context, it becomes clear that one must discriminate between SAR features that reflect the fundamental nature of the underlying molecular structures as opposed to SAR features that are merely an artifact of the chosen chemical space representation. Consequently, activity cliffs can be viewed as either fundamental or descriptor- and metrics-dependent. The latter occur as a consequence of an inappropriate molecular representation or similarity metrics and can be smoothed out by choosing a more suitable representation, e.g., by considering activity-relevant physicochemical properties. By contrast, activity cliffs fundamental to the underlying SARs cannot be circumvented by changing the reference space. In this situation, molecules that should be recognized as... 

Chemical space representation where molecules are encoded as building blocks (fragments) and linkage rules. 

Briefly, DynaMAD is designed to map database compounds to activity-specific consensus positions in chemical space representations of stepwise increasing dimensionality [38] and ACCS-FP is utilized in conventional fingerprint search calculations using multiple reference compounds [61]. 

While the mammals predominate in their integration and representation of the sensory world, their noses still tell the brain directly about its chemical space. To explain the workings of accessory olfaction, we need to trace the path of a signal molecule from the moment it leaves its source until a response occurs in the recipient. The events which occur en route will determine the effectiveness of the intended communication. 

Example 4.5 Derive the state space representation of two continuous flow stirred-tank reactors in series (CSTR-in-series). Chemical reaction is first order in both reactors. The reactor volumes are fixed, but the volumetric flow rate and inlet concentration are functions of time. 

We use this example to illustrate how state space representation can handle complex models. First, we make use of the solution to Review Problem 2 in Chapter 3 (p. 3-18) and write the mass balances of reactant A in chemical reactors 1 and 2 ... 

In chemoinformatics research, partitioning algorithms are applied in diversity analysis of large compound libraries, subset selection, or the search for molecules with specific activity (1-4). Widely used partitioning methods include cell-based partitioning in low-dimensional chemical spaces (1,3) and decision tree methods, in particular, recursive partitioning (RP) (5-7). Partitioning in low-dimensional chemical spaces is based on various dimension reduction methods (4,8) and often permits simplified three-dimensional representation of... 

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. 

Key words Chemoinformatics, QSAR, QSPR, similarity, diversity, library design, chemical representation, chemical space, virtual screening, 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. 

An alternative to dimension reduction is the use of composite and uncorrelated descriptors that are suitable for the design of information-rich yet low-dimensional chemical spaces. An elegant example is presented by the popular BCUT (Burden-CAS-University of Texas) descriptors (Pearlman and Smith 1998). BCUTs are a set of uncorrelated descriptors that combine information about molecular connectivity, inter-molecular distances, and other molecular properties. BCUT spaces used for many applications are typically only six-dimensional and can frequently be further reduced to 3D representations for visualization purposes by identifying those BCUT axes around which most compounds map. 

A primary, or unbiased, library is a large set of compounds (t5q)ically thousands to millions) based on diversity and aimed at the discovery of samples of interest for targets for which little, if any, information is available. Diversity is a concept unrelated to the library size that attempts to evaluate the representation of chemical space by a chemical library using computational methods If this space is sampled evenly by the components of a library, then this library is considered to be diversity based (Fig. 4.1, left). A focused, or biased, library is a similarity-based set of compounds (typically hundreds to thousands) aimed at the discovery and optimization of lead structures for a target for which a structural model on which to design the hbrary is available. Similarity is a concept unrelated to the library size that is opposite to diversity if the library components are clustered around the model structure A, the library is similarity based (see Fig. 4.1, right). 

Fig. 1 Representation of (a) target oriented synthesis (TOS), (b) combinatorial chemistry and (c) diversity oriented synthesis (DOS) in chemical space [2]...

The dimensionality of chemical structure space exceeds that of known biological functional space. The dimensionality of biological functional space has increased in recent years due to the discovery of a multitude of genes, largely from the Human Genome Project. This chapter, however, will focus on chemical diversity rather than functional diversity. Quantification of chemical diversity involves two areas first, the predefmition of a chemical space, accomplished by selection of a diversity metric and a compound representation (i.e., molecular descriptors) and second, a rational subset selection, or classification, method dependent on efficient dimensionality reduction. Here, we describe these methods, prerequisites for a definition... 

There is a wide variety of descriptors available to describe molecules the molecular representation they encode is key to the measurement of diversity. Descriptors directly influence the metrics and algorithms used in the design or analysis, the nature of the chemical space in question, and the location of molecules within the chemical space. Therefore, it is important to select descriptors most appropriate to the problem at hand. Such a selection of the appropriate descriptors is nontrivial, the requirement of compound representation is that it contains enough information to incorporate structure and, in some investigations, function. 

Data mining of chemical databases is still at its very early stage. Nevertheless, as a result of the data explosion in pharmaceutical industry, it is expected that data mining techniques will play an increasingly important role in the drug discovery process. Future studies may include, for example, the definition of chemical space, the validation of various algorithms (206), and the representation of extremely large virtual databases (207). 

The second point is that the new phase-space representation permits the definition of a true dividing surface in phase space which truly separates the reactant and product sides of a reaction. Traditional transition state theory of chemical reactions, based simply on coordinate-space definitions of the degrees of freedom, required an empirical correction factor, the transmission... 

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