Structure-alert information

One of the most advanced in silico predictions of any clinical liability is based on hERG QSAR and homology models6. Structural features of hERG inhibition have been discussed and published in detail, and major structural alerts are built into knowledge-based expert systems and broadly used by chemists5. Detailed information on available QSAR efforts to predict toxic effect is discussed in details elsewhere.42-43 44... 

The threshold of toxicological concern (TTC) concept has been developed to provide criteria for risk assessment decision-making in the absence of detailed information on chemicals. The approach involves estimating a tolerable human exposure value for all chemicals below which there is a very low probability of an appreciable risk to human health (Kroes et al. 2004, 2005), based on their chemical structures, compared to an extensive toxicity database. As utilized by U.S. FDA in their Threshold of Regulation procedure, structural alerts for high-potency carcinogenicity are included, to increase the assurance of safety. 

The largest imcertainty related to the model is the correct assignment of the appropriate mode of toxic action. Structural alerts, so-called toxi-cophores, have been suggested to identify compounds with specific moa [30]. However, toxicophores can only provide qualitative information on the presence or absence of a specific moa. Structural alerts can therefore not be used to estimate a metabolite s toxic potential in a quantitative way, but they play an important role in triggering experimental testing. 

Web in the life of the medicinal chemist. One may see the development of alerting services for the primary medicinal chemistry journals. The Web-based information search process could be replaced by a much more structured one based on metadata, derived by automated processing of the original full-text article. To discover new and potentially interesting articles, the user subscribes to the RSS feeds of relevant publishers and can simply search the latest items that appear automatically for keywords of interest. The article download is still necessary, but it may be possible for the client software to automatically invoke bibliographic tools to store the found references. Another application of the Chemical Semantic Web may be as alerting services for new additions to chemical databases where users get alerts for the new additions of structures or reactions. 

Identify and extract chemical compounds from the text, transform them into structures and ask an external application to compute their chemical properties and toxicology alerts, and annotate the documents with these results. The added information might then be used for further analysis of the data set. 

Upon completion of exercises 3.5 and 3.6, you probably noticed that not all written works in journals strictly adhere to the move structure in figure 3.1. Not surprisingly, the move structure does not apply to genres intended for a more general audience (e.g., news alerts, book reviews, editorial remarks), nor does it apply to all research-related works. For example, research articles published in Organic Letters omit a Methods section entirely instead, the procedures are published on the Internet as supporting information. 

SciFinder provides an easy interface to search efficiently scientific information without needing to learn complicated issues of database searching of chemical information. Tutorials are available on the web for using Explore by Research Topic, how to set up a Keep Me Posted alert to get breaking news, Browse the Table of Contents of journals, and other SciFinder features. Explore by Chemical Structure allows one to find substances based on their structure to display its physical properties, as well as information on obtaining the substance from commercial sources. CAS STN and SciFinder are considered the most extensive source of chemical information, particularly for information from the patent literature. 

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