Takihi N, Zhang Y P, Klopman G, Rosenkranz H S
Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pennsylvania 15261.
Qual Assur. 1993 Sep;2(3):255-64.
The ability to predict the biological or toxicological properties of yet untested chemicals based upon structure-activity relationships (SAR) is very dependent upon the size and chemical diversity of the "learning set" used to develop the SAR model. In the present study it is shown that for noncongeneric chemicals, systematically increasing the informational contents of "learning sets" by iteratively selecting chemicals based upon structural diversity increases the predictivity of the SAR model. This approach can now be used to generate learning sets with maximal informational content while keeping the number of chemicals that require testing at a minimum.
基于构效关系（SAR）预测未经测试化学品的生物学或毒理学特性的能力，很大程度上取决于用于开发SAR模型的“学习集”的规模和化学多样性。本研究表明，对于非同类化学品，通过基于结构多样性迭代选择化学品来系统地增加“学习集”的信息含量，可提高SAR模型的预测能力。现在可以使用这种方法来生成具有最大信息含量的学习集，同时将需要测试的化学品数量保持在最低限度。
