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The National Cancer Institute's Developmental Therapeutics Program (DTP) maintains the screening results obtained in 60 standardized cancer cell lines for ~43,000 compounds. Here the application of the categorical structure-activity relationship (cat-SAR) program for the identification of the structural attributes of identified compounds that display(More)
Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe here the application of the cat-SAR (categorical-SAR) program to two learning sets of rat mammary carcinogens. One set of developed models was based on(More)
DNA secondary structure may prove to be a significant obstacle both for enzymes that process DNA through an orifice and for the passage of DNA through nanopores proposed for some novel sequencing schemes. A nanomechanical molecular "tape reader" is assembled by threading a nanopore over DNA and pulling it using an atomic force microscope. Its formation is(More)
SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen-Non-Carcinogen (TSC-NC) and Target Site Carcinogen-Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a(More)
Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic(More)
We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were(More)
Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor(More)
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