乙酰苯胺类化合物的QSAR研究

利用量子化学程序计算了17个乙酰苯胺化合物的量子化学参数（如最高占据轨道能级、最低空轨道能级、极化率、杂原子静电荷、偶极矩等），并对化合物的最小阻滞浓度（MBC）进行了定量结构-活性相关（QSARs）分析，其中最高占据轨道能级、极化率、N（3）原子静电荷3个参数共同构建的模型准确性最高（R=0．907，F-20．064）．该模型说明化合物毒性与脂溶性关系不大，而是可能通过与受体间提供电子发挥效应，且N（3）原子为作用位点．同时利用该模型对MBC进行了理论预测，结果表明该模型能很好地预测化合物的MBC，预测值与实验值误差较小．     

硝基苯对Escherichia coli和Bacillus subtilis生长抑制的构效分析

以大肠杆菌和枯草芽孢杆菌为毒性测试生物, 测定了硝基化合物对两种细菌种群的半数生长抑制浓度值（ＥＣ_５０值）, 并对其进行定量结构活性相关性(QSARs) 研究, 分别获得多重线性回归方程, 大肠杆菌: -lg　ＥＣ_５０＝1.575+0.522lg　Ｐ+0.332I+0.341 ∑ σ^-,　 n＝27, r＝0.907, r^２=0.822, s=0.194, f=35.4; 枯草芽孢杆菌: -lg　ＥＣ_５０＝0.744lg　Ｐ+0.276 I+0.230 ^１Ｋ_a+0.179E_LUMO-0.928,　 n＝26, r＝0.964, r^２=0.928, s=0.113, f=68.1． 应用所建的QSARs模式, 预测结构相似的硝基芳烃化合物的ＥＣ_５０ 值, 并探讨了毒性作用机制.     

Back-propagation network improved by conjugate gradient based on genetic algorithm in QSAR study on endocrine disrupting chemicals

Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships （QSARs）-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals （EDCs）. The artificial neural networks （ANN） are capable of recognizing highly nonlinear relationships, so it will have a bright application prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation （BP） converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient （CG） algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This resulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q^2 pred of 0.81 and root-mean-square error （RMSE） of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.     

粒子群优化算法在定量结构活性相关性研究中的应用

粒子群优化(PSO)算法是一种新兴的优化技术，该算法简单易实现，可调参数少，已广泛应用于许多研究领域，但PSO在化学中的应用还非常少．介绍了粒子群优化算法和改进的离散粒子群优化算法的基本原理，同时结合作者科研组的工作，将其在化学构效关系研究中的应用给予简述．