Molecular descriptors computation tools

For this task, easily accessible properties of mixtures or pure metabolites are compared with literature data. This may be the biological activity spectrum against a variety of test organisms. Widely used also is the comparison of UV [90] or MS data and HPLC retention times with appropriate reference data collections, a method which needs only minimal amounts and affords reliable results. Finally, there are databases where substructures, NMR or UV data and a variety of other molecular descriptors can be searched using computers [91]. The most comprehensive data collection of natural compounds is the Dictionary of Natural Products (DNP) [92], which compiles metabolites from all natural sources, also from plants. More appropriate for dereplication of microbial products, however, is our own data collection (AntiBase [93]) that allows rapid identification using combined structural features and spectroscopic data, tools that are not available in the DNP. 

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. 

Palyulin, V.A., Baskin, I.I., Petelin, D.E. and Zefirov, N.S. (1995). Novel Descriptors of Molecular Structure in QSAR and QSPR Studies. In QSAR and Molecular Modelling Cocepts, Computational Tools and Biological Applications (Sanz, E, Giraldo, J. and Manaut, E, eds.), Prous Science, Barcelona (Spain), pp. 51-52. 

Raevsky, O.A., Dolmatova, L., Grigor ev, V.J., Hsyansky, 1. and Bondarev, S. (1995) Molecular recognition descriptors in QSAR, in QSAR and Molecular Modelling Concepts, Computational Tools and Biological Applications (eds F. Sanz, J. Giraldo and F. Manaut), Prous Science, pp. 241-245. 

With the necessary theory and background now in place, we move on to examine how to use the descriptors. In addition to what follows, the reader may wish to consult a special issue of Perspectives in Drug Discovery and Design from a few years ago entitled Computational Tools for the Analysis of Molecular Diversity. it contains review articles covering many of the issues discussed below cluster-based selection, partition-based selection, and... 

In Table 14.3, we have listed five topics that we will briefly review. The first is closely related to the notion of aromaticity, one of the central concepts of chemistry that appears to be elusive even though, as we will see, its conceptual clarification for the case of hydrocarbons was outlined over 35 years ago [6,7]. The second topic relates to the oldest statistical method, the method that has remained even today one of the most widely used tools for analysis of experimental data the multivariate regression analysis (MR A) [8]. The third topic also relates to MR A, but rather than dealing with the method itself, it is concerned with construction and selection of molecular descriptors [9]. Next, we will consider one of the topics of bioinformatics How to extract useful quantitative information from qualitative proteome maps [7]. Finally, we will address the topic of protein and DNA sequence alignments by describing, in contrast to the current computer manipulations of bio-sequences, an alternative a non-empirical graphical approach to protein and DNA sequence alignments [8-10]. 

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