Chemical class searches

In fact, when chemical class searches are chosen, the user should change as many of the "U" designations in Chemical Attributes as possible. When a U" is left, the system assigns a "worst case" value to that attribute in order to make the most conservative choice of materials. Thus, if the answer to the question, "Is the chemical a known or potential carcinogen " is "U," the system assigns it a "yes" because that is the worst case and will produce the most conservative selections when the database is evaluated for materials that have been tested against the class of compounds under consideration. 

Chemical class searches can be helpful when making selections of materials for use with chemicals which have not been tested for permeation. By extrapolating the information provided by the program for a chemical class, one can (with caution) often select materials that will have better protective qualities than those material selected without this information. However, the uncertainties illustrated from the data in Table I are inherent in any polymer selections made this way. 

For convenience of searching for information on similar compounds, chemicals are grouped under 80 chemical classes. These same classes are used with the expert system. Synonyms for many of the compounds are also included where it is likely that the synonym might simplify searching. These same synonyms are also used in the expert system. 

An initial set of Task Requirements and Chemical Attributes is generated by the program from data in the database. When searches are conducted using chemical class, however, all of the chemical attributes are initially recorded as "U" for unknown. These, along with all of the parameters in the Task Requirements segment, can be changed by the user at will. 

The results of searches of these two chemical classes matched by polymer are presented in Table II. Three candidate polymers (natural rubber, neoprene, and nitrile) were found with 15 models giving acceptable test data. 

The program has been useful in searching and summarizing all of the polymers, with specific vendor models, that have published chemical permeation data on a chemical class basis. The advantage of this is the increased the probability of selecting polymers and/or vendors models that may exhibit equal or better chemical permeation resistance to similar chemicals that have not yet been tested. Thus, fewer models may have to be tested with untested chemicals to find an acceptable garment. 

NMR 3D structure of the undecapeptide U-II generated a ligand pharmacophore hypothesis that served as query for the virtual screening of the Aventis in-house compound repository. Active leads from six different chemical classes could be identihed by the 3D search, for example, compound 17 (ECso = 400nM Fig. 16.2) [91]. 

The list of separations given here cannot be regarded as exhaustive. Many separations done in RPC are reported in such a fashion that it is difficult to discover them as RPC separations using standard methods of searching the literature. Furthermore, the reports of separations of certain chemical classes, e.g. amino acids or catecholamines, are so prevalent that only a fraction of the references were used in the interest of keeping the reference list to manageable size. 

In the first comparative exercise, 44 compounds from different chemical classes were selected (Tennant et al., 1990). An analysis of the results of the comparative exercise is provided by (Benigni, 1997). Table 8.3 reports the main features of the participating approaches. Most of the systems that participated in the comparative exercise were SAR or QS AR approaches other approaches searched for relationships between carcinogenesis and shorter-term biological events (activity-activity relationships [AARs]). 

The investigation of Taniguchi et al. [137] opened new hope in the search for medicaments in Alzheimer s disease and other taupathies such as Down s syndrome. They tested 42 compounds belonging to nine different chemical classes for their ability to inhibit the heparin-induced assembly of tau protein into filaments in vitro. Some of these compounds, including phenothiazines (methylene blue, azure A, azure B and quinacrine mustard), polyphenols (myricetin, epicatechin 5-gallate, gossypetin and 2,3,4,2, 4 -pentahydroxybenzophenone) and the porphyrin ferric deuteroporphyrin IX,... 

The module provides a search interface as well as directories for chemical classes, characteristics, and use of substances. Particularly interesting is an expert system for designing safer substances based on chemical classes. Selecting, for instance, the chemical class polymers gives access to the designing safer polymers entry, which leads to a series of questions (answers are marked in bold case) ... 

QSRR studies involving RSS are much more informative of the solute-micelle interactions. Here the separation of solutes belonging to the same chemical class, with small or even subtle structural differences, is contemplated. Optimization of the separation to result in baseline resolution of all solutes under investigation is mandatory. If the solutes are separated in a particular optimized MEKC electrolyte system, it means that the micelle is able to sense the solutes subtle structural differences. Therefore, the challenge is to search for an appropriate set of descriptors capable of explaining these differences. Considering that a specific micelle compartment is being assessed by the solute, descriptors of similar characteristics, representative of the same kind of intermolecular interaction, must be screened and selected. 

There are 38 possible individual numerical search fields, e.g. J -factor, chemical class, journal code number, space-group number, etc. Numerical tests are specified using the general construet ... 

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