Compound selection similarity

In dissimilarity-based compound selection the required subset of molecules is identified directly, using an appropriate measure of dissimilarity (often taken to be the complement of the similarity). This contrasts with the two-stage procedure in cluster analysis, where it is first necessary to group together the molecules and then decide which to select. Most methods for dissimilarity-based selection fall into one of two categories maximum dissimilarity algorithms and sphere exclusion algorithms [Snarey et al. 1997]. 

The maximum dissimilarity algorithm works in an iterative manner at each step one compormd is selected from the database and added to the subset [Kennard and Stone 1969]. The compound selected is chosen to be the one most dissimilar to the current subset. There are many variants on this basic algorithm which differ in the way in which the first compound is chosen and how the dissimilarity is measured. Three possible choices for fhe initial compormd are (a) select it at random, (b) choose the molecule which is most representative (e.g. has the largest sum of similarities to the other molecules) or (c) choose the molecule which is most dissimilar (e.g. has the smallest sum of similarities to the other molecules). 

A similarity-related approach is k-nearest neighbor (KNN) analysis, based on the premise that similar compounds have similar properties. Compounds are distributed in multidimensional space according to their values of a number of selected properties the toxicity of a compound of interest is then taken as the mean of the toxicides of a number (k) of nearest neighbors. Cronin et al. [65] used KNN to model the toxicity of 91 heterogeneous organic chemicals to the alga Chlorella vulgaris, but found it no better than MLR. 

Kogej, T., Engkvist, O., Blomberg, N., Muresan, S. Multifingerprint based similarity searches for targeted class compound selection. J. Chem. Inf. Model. 2006, 46,1201-1213. 

Selectivity studies with DTU indicated marked discrimination in the clathrate formation 23,45). As in other types of clathrates, the steric factor is important in differentiation between compounds of similar functionality but different shape. For example, DTU forms crystalline complexes with some alcohols (methanol, ethanol, propanol, 1-butanol) but not with others (2-butanol). It complexes the ethyl esters of N-acetyl derivatives of glycine, alanine, methionine and aspartic acid, but not of proline, serine, phenylalanine and glutamic acid. 

In one a priori analysis the versatility of ER modulation offers a wide array of options. These include the selective activation of either the ERa or the ER/J isoform, or the use of compounds sufficiently similar to estrogens so as to bind... 

A similar strategy was employed to identify a DPP-IV inhibitor (6) with good in vivo potency in a mouse model of diabetes [44], Plasma protein binding, as assessed by shift assay (50% serum), was presented for all final compounds. The compound selected as having the best overall profile was active in vivo at 0.1 mg/kg. The activity at 1 h post-dose was consistent with the free drug principle - total plasma concentration 269 nM murine-free fraction 4% unbound plasma concentration 11 nM in vitro potency versus murine DPP-IV 6nM. 

Another possible use of in vitro developmental toxicity tests would be to select the least developmentally toxic backup from among a group of structurally related compounds with similar pharmacological activity [use (2) in the list above], for example, when a lead compound causes malformations in vivo and is also positive in a screen that is related to the type of malformation induced. However, even for this limited role for a developmental toxicity screen, it would probably also be desirable to have a measure of the comparative matemotoxicity of the various agents and/or information on the pharmacokinetics and distribution of the agents in vivo. 

Mass spectrometers are among the most selective detectors, but they are still susceptible to interferences. Isomers have identical spectra, whereas many other compounds have similar mass spectra. Heavy petroleum products can contain thousands of major components that are not resolved by the gas chromatograph. As a result, multiple compounds enter the mass spectrometer simultaneously. Different compounds may share many of the same ions, confusing the identification process. The probability of misidentiflcation is high in complex mixtures such as petroleum products. 

Aquatic animals use their chemical senses in all aspects of their lives, from reproductive behavior to feeding, habitat selection, and predator avoidance. The hydrodynamic properties determine the possibilities and limits of chemical communication in water. As a medium, water is as dynamic as air, so that convection and advection are far more important for odor transport than is diffusion. Distribution by currents is even more important in water because compounds of similar molecular weight diffuse four orders of magnitude more slowly than in air (Gleeson, 1978). Diffusion of odorants may be important only in the submillimeter range, while turbulence is typical for water masses above the centimeter range. 

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