Molecular descriptors applications

Djakovic-Sekulic, T. Perisic-Janjic, N. Pyka, A. Correlation or retention of anilides and some molecular descriptors. Application of topological indexes for prediction of log k values. Chromatographia 2003, 58 (1/2), 47-51. Pyka, A. Sliwiok, J. Chromatographic separation of tocopherols. J. Chromatogr., A 2001, 935, 71-76. 

Source From Correlation or retention of anilides and some molecular descriptors. Application of topological indexes for prediction of log k values, in Chromatographia. - ... 

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. 

An alternative viewpoint for structure-activity investigations is to utilize quantitative models as probes into the mechanism of action of the set of compounds being studied. In this case it is most useful if the molecular descriptors are explicitly meaningful in terms of chemical reactivity or physiological behavior, e.g., distribution of the compound in an organism (see Table II). In a previous symposium, (18), we described our application of this approach toward the development of a quantitative structure-potency expression, equation 1,... 

Duchowicz PR, Castro EA, Toropov AA, Benfenati E (2006) Applications of Flexible Molecular Descriptors in the QSPR-QSAR Study of Heterocyclic Drugs. 3 1-38... 

D., Balahan, A. T. Comparison of weighting schemes for molecular graph descriptors application in quantitative structure-retention relationship models for alkylphenols in gas-liquid chromatography. J. Chem. Inf. Comput. Sci. 2000, 40, ITl-lM,. 

The retention depends on the nature of both the stationary phase and the organic modifier in the mobile phase. Therefore CHI values obtained using different systems show different sensitivities towards solute characteristics. This has been studied systematically and used for the quantitative calculation of solute molecular descriptors (H-bond donor capacity, H-bond acceptor capacity and dipolarity/polarizability) for application in a general solvation equation [21]. 

A fundamental problem encountered in these correlations is the mismatch between the accuracy of experimental data and the molecular descriptors which can be calculated with relatively high precision, usually within a few percent. The accuracy may not always be high, but for correlation purposes precision is more important than accuracy. The precision and accuracy of the experimental data are often poor, frequently ranging over a factor of two or more. Certain isomers may yield identical descriptors, but have different properties. There is thus an inherent limit to the applicability of QSPRs imposed by the quality of the experimental data, and further efforts to improve descriptors, while interesting and potentially useful, may be unlikely to yield demonstrably improved QSPRs. 

J.A., and Wilson, T.M. Selection, application, and validation of a set of molecular descriptors for nuclear receptor ligands. Book of Abstracts,... 

To overcome this weakness, we are developing a quantitative structure-activity strategy that is conceptually applicable to all chemicals. To be applicable, at least three criteria are necessary. First, we must be able to calculate the descriptors or Independent variables directly from the chemical structure and, presumably, at a reasonable cost. Second, the ability to calculate the variables should be possible for any chemical. Finally, and most importantly, the variables must be related to a parameter of Interest so that the variables can be used to predict or classify the activity or behavior of the chemical (j ) One important area of research is the development of new variables or descriptors that quantitatively describe the structure of a chemical. The development of these indices has progressed into the mathematical areas of graph theory and topology and a large number of potentially valuable molecular descriptors have been described (7-9). Our objective is not concerned with the development of new descriptors, but alternatively to explore the potential applications of a group of descriptors known as molecular connectivity indices (10). 

In 2008, Weaver [64] utilized PPB as an example to demonstrate the concept of "domain of applicability" in QSAR researches. The PLS model was constructed using 17 ID and 2D molecular descriptors. The performance of the model was reasonable for such a large data set for PPB modeling (n — 685, q2 — 0.56, RMSE = 0.55 AUE = 0.42, ntest = 210, q2 = 0.58, RMSEtest = 0.54, AUEtest = 0.41). How domain selection protocol affects the prediction performance will be discussed in Section 3. 

Once a QSAR model is constructed, it must be validated using the external test set. The data points in the test set should not appear in the training set. There are two approaches to improve the prediction accuracy for a given QSAR model. The first approach utilized the concept of "the domain of applicability," which is used to estimate the uncertainty in prediction of a particular molecule based on how similar it is to the compound used to build the model. To make a more accurate prediction for a given molecule in the test set, the structurally similar compounds in the training set are used to construct model and that model is used to make the prediction. In some cases, the domain similarity is measured using molecular descriptor similarity, rather than the structural similarity. The... 

Derivation and Applications of Molecular Descriptors Based on Approximate Surface Area... 

In the present work, we will use a relatively low level of theory to derive 32 weakly correlated molecular descriptors, each based on the subdivision and classification of the molecular surface area according to three fundamental properties contribution to ClogP, molar refractivity, and atomic partial charge. The resulting collection will be shown to have applicability in QSAR, QSPR, and compound classification. Moreover, the derived 32 descriptors linearly encode most of the information of a collection of traditional mathematical descriptors used in QSAR and QSPR. 

A major practical issue affecting MP calculations is caused by use of correlated molecular descriptors. During subsequent MP steps, exact halves of values (and molecules) are only generated if the chosen descriptors are uncorrelated (orthogonal), as shown in Fig. 1A. By contrast, the presence of descriptor correlations (and departure from orthogonal reference space) leads to overpopulated and underpopulated, or even empty, partitions (see also Note 5), as illustrated in Fig. ID. For diversity analysis, compounds should be widely distributed over computed partitions and descriptor correlation effects should therefore be limited as much as possible. However, for other applications, the use of correlated descriptors that produce skewed compound distributions may not be problematic or even favorable (see Note 5). 

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. 

Molecular descriptors vary gready in both their origins and their applications. They come from both experimental measurements and theoretical computations. Typical molecular descriptors from experimental measurements include logP, aqueous solubility, molar refractivity, dipole moment, polarizability, Hammett substituent constants, and other empirical physicochemical properties. Notice that the majority of experimental descriptors are for entire molecules and come directly from experimental measurements. A few of them, such as various substituent constants, are for molecular fragments attached to certain molecular templates and they are derived from experimental results. 

Applications of molecular descriptors are as diverse as their definitions. The important classes of applications include QSAR and/or QSPR, similarity, diversity, predictive models for virtual screening and/or data mining, data visualization. We will discuss briefly some of these applications in the next sections. 

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