应用分子全息QSAR预测多溴二苯醚(PBDEs)的毒性

多溴二苯醚(PBDEs)可能会激活芳香烃受体的信号传导通路, 从而对人类和野生动物的健康产生负面影响. 鉴于多溴二苯醚实验毒性数据有限, 发展基于结构的化合物毒性预测模型具有重要的实际意义. 本文基于一种新的分子结构表征方法—— 分子全息, 研究了18种多溴二苯醚结构与毒性之间的关系, 建立了相关性显著、稳健性强的QSAR模型(r²= 0.991, q²_LOO= 0.917). 随机选出14种多溴二苯醚为训练集, 其他4种化合物为测试集以验证分子全息QSAR模型的稳健性和预测能力. 结果在最佳建模条件下得到模型的统计参数如下：r² = 0.988, q²_LOO = 0.598, r²_pred = 0.955, 预测值与实验值之间的均方根误差(RMSE)为0.155. 这表明基于分子全息的QSAR模型可以对多溴二苯醚毒性进行比较准确的预测. 本文同时利用分子全息QSAR模型色码图, 探讨了影响多溴二苯醚毒性的分子结构特征及分子机理.     

On the use of computational neural networks for the prediction of polymer properties

A general purpose computational paradigm using neural networks is shown to be capable of efficiently predicting properties of polymeric compounds based on the structure and composition of the monomeric repeat unit. Results are discussed for the prediction of the heat capacity, glass transition temperature, melting temperature, change in the heat capacity at the glass transition temperature, degradation temperature, tensile strength and modulus, ultimate elongation, and compressive strength for 11 different families of polymers. The accuracies of the predictions range from 1–13% average absolute error. The worst results were obtained for the mechanical properties (tensile strength and modulus: 13%, 7% elongation: 12%, and compressive strength: 8%) and the best results for the thermal properties (heat capacity, glass transition temperature, and melting point: <4%). A simple modification to the overall method is devised to better take into account the fact that the mechanical properties are experimentally determined with a fairly large range (due to variability in measurement procedures and especially the sample). This modification treats the bounds on the range for the mechanical properties as complex numbers (complex, modular neural networks) and leads to more rapid optimization with a smaller average error (reduced by 3%).Dedicated to Professor Bernhard Wunderlich on the occasion of his 65th birthdayThis research was sponsored by the Division of Materials Sciences, Office of Basic Energy Sciences, U.S. Department of Energy, under Contract No. DE-AC05-84R21400 with Lockheed Martin Energy Systems, Inc. We would like to express our gratitude for the continued collaboration, support, and interest of Prof. Wunderlich in our research. We would also like to thank participants of the 1st DOE Workshop on Applications of Neural Networks in Materials Sciences for useful discussion on materials properties and neural networks.