松花江水中有机化学品的生物毒性预测

应用人工神经网络技术构建的定量构效关系模型(QSAR)对存在松花江水中的有机化学品的毒性进行了预测.结果显示,有机化学品对酵母菌毒性的预测值与实测值接近,预测误差较小.另外,采用所建立的有机化学品对酵母菌毒性和对呆鲦鱼毒性的相关性方程预测了松花江水中有机化学品对呆鲦鱼的毒性,结果显示,有机化学品对酵母菌的毒性与对呆鲦鱼的毒性之间具有很好的相关性(R=0.9826),从整个有机化学品对呆鲦鱼毒性大小的趋势上看,预测值与实验值基本吻合.因此,得出采用低等微生物—酵母菌替代高等生物—鱼作为指示生物进行有机化学品毒性的评价是可行的,这一研究成果对大量有机化学品毒性筛选和进行优先污染物控制方面具有重要的意义.

Study on bio-toxicity prediction of organic chemicals in Songhua River

Using quantitative structure-activity relationship (QSAR) model built by artificial neural network (ANN) predict the bio-toxicity of organic chemicals in Songhua River. The results showed that prediction value of bio-toxicity of organic chemicals on yeast saccharomyces cerevisiae is approximately equal to test value, and the prediction error is less. In addition, the toxicity of organic chemicals on fathead minnow is also predicted using correlation equation between the toxicity of organic chemicals on yeast saccharomyces cerevisiae and on fathead minnow. The results showed that there is the better relativity between the toxicity of organic chemicals on yeast saccharomyces cerevisiae and on fathead minnow, and the correlation coefficient =(0.982 6). From the toxicity of different organic chemicals on fathead minnow, prediction vale is near to test value. So using yeast Saccharomyces cerevisiae instead of fish as indicator organism is feasible. The study is significant for screening organic chemicals and controlling priority pollutants.