Toropov Andrey Andreevich, Schultz Terry Wayne
Vostok Holding Innovation Co. 700000, Sadik Azim 4th Street, 15 Tashkent, Uzbekistan.
J Chem Inf Comput Sci. 2003 Mar-Apr;43(2):560-7. doi: 10.1021/ci025555n.
Quantitative structure-activity relationships (QSARs) were developed for three sets of toxicity data. Chemicals in each set represented a number of narcoses and electrophilic mechanisms of toxic action. A series of quantitative structure-toxicity models correlating toxic potency with a number of optimization of correlation weights of local graph invariants were developed. In the case of the toxicity of a heterogeneous set of benzene derivatives to Tetrahymena pyriformis, the QSARs were based on the Descriptor of Correlation Weights (DCW) using atoms and extended connectivity (EC) graph invariants. The model [log (IGC(50)(-1)) = 0.0813 DCW(a(k),(3)EC(k)) + 2.636; n = 157, r(2) = 0.883, s = 0.27, F = 1170, Pr > F = 0.0001] based on third-order EC of 89 descriptors was observed to be best for the benzene data. However, fits for these data of > 0.800 were achieved ECs with as few as 23 variables. The relationship between the toxicity predicted by this model and experimental toxicity values for the test set [obs. log(IGC(50)(-1))) = 0.991 (pred. (log(IGC(50)(-1))) - 0.012; n = 60, r(2) = 0.863, s = 0.28, F = 372, Pr > F = 0.0001] is excellent. The utility of the approach was demonstrated by the model [log (IGC(50)(-1)) = 0.1744(DCW (a(k), (2)EC) - 3.505; n = 39, r(2) = 0.900, s = 0.35, F = 333, Pr > F = 0.0001] for the toxicity data for T. pyriformis exposed to halo-substituted aliphatic compounds and the model [log (IC(50)(-1)) = 0.1699(DCW (a(k), (2)EC)) - 2.610; n = 66, r(2) = 0.901, s = 0.31, F = 583, Pr > F = 0.0001] for the Vibrio fischeri toxicity data.
针对三组毒性数据建立了定量构效关系（QSARs）。每组中的化学物质代表了多种麻醉作用和毒性作用的亲电机制。开发了一系列将毒性效力与局部图不变量的相关权重的多个优化值相关联的定量结构-毒性模型。对于一组异构的苯衍生物对梨形四膜虫的毒性，QSARs基于使用原子和扩展连接性（EC）图不变量的相关权重描述符（DCW）。基于89个描述符的三阶EC的模型[log(IGC(50)(-1)) = 0.0813 DCW(a(k),(3)EC(k)) + 2.636; n = 157, r(2) = 0.883, s = 0.27, F = 1170, Pr > F = 0.0001]被观察到对苯数据是最佳的。然而，对于这些数据，使用少至23个变量的EC也能实现大于0.800的拟合度。该模型预测的毒性与测试集的实验毒性值之间的关系[obs. log(IGC(50)(-1))) = 0.991 (pred. (log(IGC(50)(-1))) - 0.012; n = 60, r(2) = 0.863, s = 0.28, F = 372, Pr > F = 0.0001]非常出色。该方法的实用性通过针对暴露于卤代脂肪族化合物的梨形四膜虫毒性数据的模型[log(IGC(50)(-1)) = 0.1744(DCW (a(k), (2)EC) - 3.505; n = 39, r(2) = 0.900, s = 0.35, F = 333, Pr > F = 0.0001]和针对费氏弧菌毒性数据的模型[log(IC(50)(-1)) = 0.1699(DCW (a(k), (2)EC)) - 2.610; n = 66, r(2) = 0.901, s = 0.31, F = 583, Pr > F = 0.0001]得到了证明。
