By Andrew R. Leach, V. J. Gillet
This booklet goals to supply an creation to the main suggestions of chemoinformatics. it's the first textual content written in particular for this box. the 1st a part of the publication offers with the illustration of second and 3D molecular constructions, the calculation of molecular descriptors and the development of mathematical types. the second one half describes different very important subject matters together with molecular similarity and variety, the research of huge info units, digital screening, and library layout. uncomplicated illustrative examples are used all through to demonstrate key techniques, supplemented with case reports from the literature.
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Extra info for An Introduction to Chemoinformatics
We then consider the concept of a 3D pharmacophore. This leads to the use of 3D databases derived purely from theoretical calculations, methods for generating pharmacophores and the identification of potentially useful molecules using 3D pharmacophore searching. Some background material concerning molecular conformations and energy calculations can be found in Appendix 2. 27 28 2. An Introduction to Chemoinformatics EXPERIMENTAL 3D DATABASES The data stored in a 3D database either comes from experiment or can be calculated using a computational method.
6. 2. 8. 3. 4. 1. R2 X=Cl, Br Figure 1-14. An example of atom mapping, in this case for the formation of aminothiazoles from thioureas and α-haloketones. 20 An Introduction to Chemoinformatics Given the volume of published material, it is a significant task to maintain those databases which survey all or a significant proportion of the chemical literature. For example, the Beilstein database contains more than 9 million reactions [Meehan and Schofield 2001; Crossfire] and the CASREACT database more than 6 million reactions [Blake and Dana 1990; CASREACT].
The second problem concerns the fact that there may be many different combinations of pharmacophoric groups within the molecules. As a consequence, there may be hundreds of potential 3D pharmacophores. The objective is to determine which of these potential pharmacophores best fits the data. In general, the aim is to identify the 3D pharmacophore(s) that contains the largest number of features common to all of the active molecules, and where these common features can be presented by each molecule in a low-energy conformation.
An Introduction to Chemoinformatics by Andrew R. Leach, V. J. Gillet