Compound selection techniques

Furthermore, genetic algorithms [92-95], neural networks [73] or simulated annealing [96] have also been applied for selecting appropriate sublibraries. 

---

Lajiness, M.S. Dissimilarity-based compound selection techniques. Perspect. Drug Discov. Des., 1997, 7/8, 65-84. 

In addition to physical properties, substructure-based filters can be applied to reduce further the number of molecules, for instance molecules with undesirable functionality for example, reactive or toxic groups can be removed and molecules with particular features (or atoms) can be actively selected. There may be particular functionality that it is desirable to avoid due to assay format, such as fluorophores in fluorescence-based approaches. Structural features for these inclusion and exclusion criteria can be readily formulated using SMILES-based procedures and this type of substructure-based compound selection technique can also be employed in the generation of focused sets of fragment molecules. 

Matter [58] has also validated a range of 2-D and 3-D structural descriptors on their ability to predict biological activity and on their ability to sample structurally and biologically diverse datasets effectively. The compound selection techniques used were maximum dissimilarity and clustering. Their results also showed the 2-D fingerprint-based descriptors to be the most effective in selecting representative subsets of bioactive compounds. 

M. S. Lajiness, Perspect. Drug Discovery Des., 7/8, 65 (1997). Dissimilarity-Based Compound Selection Techniques. 

Lajiness M (1990) Molecular similarity-based methods for selecting compounds for screening. In Rouvray D (ed) Computational chemical graph theory. Nova Science, pp 299-316 Lajiness MS (1997) Dissimilarity-based compound selection techniques. Perspect Drug Disc Design 7/8 65 84... 

The previous sections have summarized the basic techniques available for searching chemical databases for specific types of query. Another important database application is compound selection, the ability to select a subset of a database for submission to a biological testing program. The selection procedure can be applied to in-house databases, to externally available compound collections, or to virtual libraries, that is, sets of compounds that could potentially be synthesized. 

Downs GM, Willett P. Clustering of chemical structure databases for compound selection. In van de Waterbeemd H, editor, Advanced computer-assisted techniques in drug discovery. Weinheim VCH Verlag, 1994. p. 111-30. 

The use of GC-MS in polymer/additive analysis is now well established. Various GC-based polymer/additive protocols have been developed, embracing HTGC-MS, GC-HRMS and fast GC-MS with a wide variety of front-end devices (SHS, DHS, TD, DSI, LD, Py, SPE, SPME, PTV, etc.). Ionisation modes employed are mainly El, Cl (for gases) and ICPI (for liquid and solid samples). Useful instrumental developments are noticed for TD-GC-MS. GC-SMB-MS is a fast analytical tool as opposed to fast chromatography only [104]. GC-ToFMS is now about to take off. GC-REMPI-MS represents a 3D analytical technique based on compound-selective parameters of retention time, resonance ionisation wavelength and molecular mass [105]. 

The analytes are typically extracted from the biological matrix using solvent extraction or solid phase extraction (SPE). Most analytes require some form of chemical derivatization prior to analysis by GC-MS techniques, whereas with LC-MS-MS no further treatment of the extract is required. The extracts obtained from urine are relatively dirty because of the many endogenous compounds that are present. It is for this reason that the very selective techniques of GC-MS-MS, GC-HRMS, or LC-MS-MS are required to detect some of the prohibited substances that have low detection levels. 

A new, fast, sensitive, and solventless extraction technique was developed in order to analyze beer carbonyl compounds. The method was based on solid-phase microextraction with on-fiber derivatization. A derivatization agent, 0-(2,3,4,5,6-pentafluorobenzyl) hydroxylamine (PFBOA), was absorbed onto a divinyl benzene/poly(dimethylsiloxane) 65- xm fiber and exposed to the headspace of a vial with a beer sample. Carbonyl compounds selectively reacted with PFBOA, and the oximes formed were desorbed into a gas chromatograph injection port and quantified by mass spectrometry. This method provided very high reproducibility and linearity When it was used for the analysis of aged beers, nine aldehydes were detected 2-methylpropanal, 2-methylbutanal, 3-methylbutanal, pentanal, hexanal, furfural, methional, phenylacetaldehyde, and (E)-2-nonenal. (107 words)... 

This chapter reviews the techniques available for quantifying the effectiveness of methods for molecular similarity and molecular diversity, focusing in particular on similarity searching and on compound selection procedures. The evaluation criteria considered are based on biological activity data, both qualitative and quantitative, with rather different criteria needing to be used depending on the type of data available. 

In search of bioactive substances, researchers have directed their interest towards substances found in plants. Parts of plants which have been used in natural medicine have proved to be a rich source of bioactive compounds however, to make use of them, they have to be isolated and their properties deterrnined. Using selective techniques of extraction has resulted in obtaining concentrated preparations of bioactive substances. To achieve comprehensive knowledge of their properties, it was necessary to develop methods of isolation of individual components and testing these methods. This could be done with chromatographic techniques. Isolated compounds were tested in order to show which of them (and to what extent) are responsible for bioactivity of plant preparations from which they were obtained. Due to the fact that many of the substances have the opposite effect, it is frequently impossible to use extracts without isolating individual compounds. 

---