Diverse Sets of Compounds

A key requirement of QSAR is that the compounds used in the modeling and prediction processes should have the same mechanism of action, and for this reason most QSAR studies are made with congeneric series of compounds. However, if a diverse set of compounds can reasonably be assumed to have the same mechanism of action, QSAR modeling can justihably be carried out. For example, Dearden et al. [43] developed a QSAR for the ratio of brain levels of 22 very diverse drugs in the wild-type mouse and the P-glycoprotein knockout mouse (R+/ ) ... 

A widely used 3-D QSAR method that makes use of PLS is comparative molecular field analysis (CoMFA), in which a probe atom is used to calculate the steric and electronic fields at numerous points in a 3D lattice within which the molecules have been aligned. Poso et al. [56] used the technique to model the binding of coumarins to cytochrome P450 2A5, with similar results to those obtained by Bravi and Wikel [55]. Shi et al. [57] used it to model the estrogen receptor binding of a large diverse set of compounds, and Cavalli et al. [58] used it to develop a pharmacophore for hERG potassium... 

Ligands that interact physically with DNA have been extensively studied both by experimental techniques and by a variety of theoretical approaches. A diverse set of compounds have been studied, including compounds that intercalate between DNA sequences or bind in the minor groove.1 7 These studies have identified various factors that influence the stability of DNA ligand complexes in solution.6 8 9... 

Researchers at SRMLSC recently developed a HTS that allowed the identification of potential inhibitors of the severe acute respiratory syndrome coronavirus (SARS CoV) from large compound libraries [34], The luminescent-based assay, which measured the inhibition of SARS CoV-induced cytopathic effects (CPE) in Vero E6 cells, was validated with two different diversity sets of compounds against the SARS CoV. The hit rate for both libraries was approximately 0.01%. 

A number of different approaches have been used for selecting diverse sets of compounds. One of the simplest is maximum dissimilarity, in which each new compound is chosen to be as dissimilar as possible to those already selected [60]. This can drastically reduce the library size without significantly reducing the likelihood of discovering classes... 

Willett, P. Dissimilarity-based algorithms for selecting structurally diverse sets of compounds./. Comput. Biol. 1999, 6, 447-457. 

Cheng, A. and Merz, K.M. Jr. Prediction of aqueous solubility of a diverse set of compounds using quantitative stmcture-property relationships. /. Med. Chem. 2003, 46, 3572-3580. 

In the early stages of the project when leads do not exist, computational methods can be used to select a diverse set of compounds from a large virtual library. If a compound shows activity then other similar compounds from the library are synthesised and tested. If a lead already exists at the start of the programme, the size of the virtual library can be reduced by selecting a subset of compounds that are similar to the lead. 

There are two bases for the comparison of similarity and diversity methods. It is possible to compare the efficiency of methods, i.e., the resources, typically computer time and computer memory, necessary for the completion of processing. Considerations of efficiency, in particular, theoretical analyses of computational complexity, are important in that they can serve to identify methods that are unlikely to be applicable given the rapidly increasing sizes of current and planned chemical datasets. Here, however, we restrict ourselves to comparing the effectiveness of similarity and diversity methods, i.e., the extent to which a method is able to satisfy the user s requirements in terms of identifying similar or diverse sets of compounds. More specifically, we focus on evaluation criteria based on the availability of bioactivity data for the molecules that are being processed, where the data can either be qualitative, i.e., a categorical (usually binary) variable, or quantitative, i.e., a real-valued variable. The discussion here considers only the criteria that can be used for comparative studies the reader is referred elsewhere for the results of such studies. 

Once interest has been focused on some small volume of structural space, large numbers of molecules are synthesized and tested (and often re-tested in the case of HTS data), and the results of these experiments used to develop a quantitative structure-activity relationship (QSAR). It has for long been claimed that the use of diverse sets of compounds will enable more robust QSARs to be... 

As mentioned above, it is now common for pharmaceutical companies to select a diverse set of compounds for screening that represent the available compounds (either internal or commercially available). Yet, this is not so for compound collections that have grown as sets of compounds, have been synthesized, or have been acquired for particular projects. This is an important reason why many examples of data-mining techniques when applied to the NCI dataset work so well and may help explain why such methods often do not perform as well against different HTS datasets (22). 

One method of choosing a diverse set of compounds from a molecular database is to first cluster them (see, for example ref. 12) and then select one molecule from each cluster. Clustering ideally groups the molecules into well-separated, compact groups with respect to the descriptor variables. If this is the case, then each cluster or group can be represented by one of its members. This method of choosing a diverse set of objects goes back at least to Zemroch (13) in the statistics literature and has been widely used for chemical databases (14,15). 

Since diversity is a collective property, its precise quantification requires a mathematical description of the distribution of the molecular collection in a chemical space. When a set of molecules are considered to be more diverse than another, the molecules in this set cover more chemical space and/or the molecules distribute more evenly in chemical space. Historically, diversity analysis is closely linked to compound selection and combinatorial library design. In reality, library design is also a selection process, selecting compounds from a virtual library before synthesis. There are three main categories of selection procedures for building a diverse set of compounds cluster-based selection, partition-based selection, and dissimilarity-based selection. 

Stanton and Jurs [3] developed a model for a more diverse set of compounds, including hydrocarbons, halogenated hydrocarbons, alkanols, ethers, ketones, and esters. The model has been evaluated with 31 compounds, using, among others, charge partial surface area (CPSA) descriptors ... 

Estimation of Melting Points As indicated above, the development of structure-rm relationships is not as straightforward as it is for other properties. In the following sections we discuss briefly the estimation of Tm for homologous series and for other sets of structurally related compounds. A GCM designed to estimate Tm for more diverse sets of compounds is introduced. Although not very accurate, the GCM approach may be applicable for the following tasks ... 

QSAR studies are normally carried out on groups of related compounds. However, QSAR studies on structurally diverse sets of compounds are becoming more common. In both instances it is important to consider as wide a range of parameters as possible. 

Chapman [44] describes a method for selecting a diverse set of compounds that is based on 3-D similarity. The diversity of a set of compounds is computed from the similarities between all conformers in the dataset, where multiple conformers are generated for each structure. The similarity between two conformers is determined by aligning them and measuring how well they can be superimposed in terms of steric bulk and polar functionalities. A diverse subset is built by adding one compound at a time and the compound that would contribute the most diversity to the subset is chosen in each step. The high computational cost of this method restricts its use to small datasets. 

Rose VS, Rahr E, Hudson BD, The use of Procrustes analysis to compare different property sets for the characterisation of a diverse set of compounds, Quant. Struct-Act. Relat., 13 152-158, 1994. 

This chapter is concerned with some of the background theory for molecular diversity analysis and includes a discussion of diversity indices, intermolecular similarity and dissimilarity measures. The extent to which the different approaches to diversity analysis have been validated and compared is reviewed. Algorithms for the selection of diverse sets of compounds are covered in detail elsewhere in this book and are mentioned only briefly here. However, consideration is given to whether these algorithms should be applied in reactant or product space. 

Chapman [55] describes a method for selecting a diverse set of compounds that is based on 3D similarity. The diversity of a set of... 

Many different methods have been developed both to measure diversity and to select diverse sets of compounds, however, currently there is no clear picture of which methods are best. To date, some work has been done on comparing the various methods however, there is a great need for more validation studies to be performed both on the structural descriptors used and on the different compound selection strategies that have been devised. In some cases, the characteristics of the library itself might determine the choice of descriptors and the compound selection methods that can be applied. For example, computationally expensive methods such as 3D pharmacophore methods are limited in the size of libraries that can be handled. Thus for product-based selection, they are currently restricted to handling libraries of tens of thousands of compounds rather than the millions that can be handled using 2D based descriptors. 

Rose, V.S., Rahr, E. and Hudson, B.D. The Use of Procrustes Analysis to Compare Different Property Sets for the Characterisation of a Diverse Set of Compounds. QSAR, 1994,13, 152-158. 

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