摘要：A set of 21 mutagenic aliphatic N-nitrosamines were subjected to a pattern recognition analysis using ADAPT software. Four descriptors based on molecular connectivity, geometry and sigma charge on nitrogen were capable of achieving a 100% classification using the linear learning machine or iterative least squares algorithms. Three descriptors were capable of a 90.5% and two descriptors of a 85.7% overall correct classification. Three of the four descriptors were each capable of classifying 15 of the 16 active chemicals while it required three of the four descriptors to classify correctly two of the five inactive chemicals. These results are in concert with previous observations that molecular connectivity, geometry, and sigma charge on nitrogen are powerful descriptors for separating active from inactive mutagenic and carcinogenic N-nitrosamines. Full text Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (789K), or click on a page image below to browse page by page. Links to PubMed are also available for Selected References . 345 346 347 348 349
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