Chemoinformatics compounds

Having settled on a definition of chemoinformatics, it is time for us to reflect on the distinction between chemoinformatics and bioinformatics. The objects of interest of bioinformatics are mainly genes and proteins. But genes, DNA and RNA, and proteins are chemical compounds They are objects of high interest in chemistry, Chemists have made substantial contributions to the elucidation of the structure and function of nucleic adds and proteins. The message is dear there is no clearcut distinction between bioinfonnatics and chemoinformatics I... 

Clearly, by tradition, chemoinformatics has largely dealt with small molecules, whereas bioinformatics has started to move from genes to proteins, compounds... 

In the case of chemoinformatics this process of abstraction will be performed mostly to gain knowledge about the properties of compounds. Physical, chemical, or biological data of compounds will be associated with each other or with data on the structure of a compound. These pieces of information wQl then be analyzed by inductive learning methods to obtain a model that allows one to make predictions. 

Foremost we hope - and believe - that chemoinformatics will become of increasing importance in the teaching of chemistry. The instruments and methods that are used in chemistry will continue to swamp us with data and we have to manage these data to increase our chemical knowledge. We have to understand more deeply, and exploit, the results of our experiments. Concomitantly, demands on the properties of the compounds that are produced by the chemical and pharmaceutical industries will continue to rise. We will need materials that are better we need them to be more selective, have fewer undesirable properties, able to be broken down easily in the environment without producing toxic by-products, and so on. This asks for more insight into the relationships between chemical structures and their properties. Furthermore, we have to plan and perform fewer and more efficient experiments. 

With the increase in hardware and software, larger systems can be handled with higher accuracy. Much work will continue to be devoted to the study of proteins and polynucleotides (DNA and RNA), and particularly their interactions with more sophisticated methods. Remember proteins and genes are chemical compounds and sophisticated theoretical and chemoinformatics methods should be applied to their study - in addition to the methods developed by bioinfor-maticians. 

Two of the other factors that are important to many chemists are patentability and the number of active compounds in the set of validated hits. Current chemoinformatics tools are not well suited to predict these factors. 

According to our intention, to provide in this series on Methods and Principles in Medicinal Chemistry practice-oriented monographs, the book closes with a section on Chemoinformatics Applications. These are exemplified by G.M. Maggiora et al. in a chapter on A Practical Strategy for Directed Compound Acquisition , by... 

The SOSA approach can be enhanced by virtual screening methods which use reference compound sets and molecular descriptors together with advanced chemoinformatics methods to compare and rank the similarity of considered candidate molecules. 

Maggiora GM, Shanmugasundaram V, Lajiness MS, et al. (2005) A practical strategy for directed compound acquisition. In T Oprea (ed), Chemoinformatics in Drug Discovery, pp. 317-332. Wiley-VCH, Weinheim. 

The group of Prof Peter Willett from the University of Sheffield summarizes in Chapter 6 new chemoinformatics methods for similarity-based virtual screening which based on known active compounds are useful for the identification of new ligands for targets related by conserved molecular recognition. 

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... 

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