Predicting hexadecane–air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data

A group contribution-based quantitative structure–property relationship (QSPR) for the hexadecane–air equilibrium partition coefficients (L) of organic chemicals is developed using the iterative fragment selection (IFS) approach. This new QSPR includes in its training and external validation data sets L values for a large number of structurally complex chemicals measured by the same group using consistent methods. The resulting QSPR has better predictive power than other prediction methods trained primarily using data for chemicals of simpler structures, and measurements of L values from diverse sources. For a subset of chemicals in which the L values have non-additive effects caused by intramolecular hydrogen bonds, the new QSPR gives much better performance in comparison to the most commonly used prediction method.     

QSAR/QSPR在POPs归趋与风险评价中的应用*

持久性有机污染物(POPs)是目前备受国际社会关注的高危害性有机污染物,对它们的环境归趋分析和风险评价需要获得大量可靠的性质数据和毒性数据,而定量结构活性/性质相关(QSAR/QSPR)方法为快速有效地获得这些数据提供了可能性。QSAR/QSPR模型已在预测POPs的生物活性/性质,补充缺失的基础数据及探求POPs的环境过程机制和生态效应机理等方面得到了广泛应用,近年来也在新POPs物质的筛选、归趋模拟以及风险评价等方面有着更进一步的应用或潜在应用前景。本文介绍了QSAR/QSPR在POPs性质和生物活性预测中的基本应用及其在POPs归趋和风险评价中的扩展应用,并对QSAR/QSPR在POPs研究领域的应用前景进行了展望。     

用定量结构性质关系预测表面活性剂的浊点

用结构、电子、空间和热力学等性质为描述符，建立了宏观性质非离子表面活性剂浊点与微观结构之间的定量结构性质关系. 这些描述符包括辛醇/水分配系数lgP、分子表面积A、相对分子质量Mr、偶极矩μ-y和μ-z.通过49个非离子表面活性剂浊点的实验数值与五个性质之间的关系式，成功地预测了其他非离子表面活性剂的浊点.     

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.