苯酚和苯胺类化合物对大型蚤毒性的QSAR

基于DFT-B3LYP方法在6-311G**基组水平上计算获得了36种苯酚和苯胺类化合物的量化和理化参数,采用最佳变量子集回归方法,建立苯酚和苯胺类化合物对大型蚤毒性与上述描述符参数的定量构效关系。结果表明所建三变量模型具有良好的预报能力和稳健性,化合物次最低空轨道能愈小,毒性愈大;化合物疏水性愈强,毒性愈大;化合物偶极距愈小,毒性愈强。     

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.     

氯代芳烃对戈卑鱼毒性的定量构效关系解析

基于定量结构-活性相关（QSAR）研究氯代芳烃的性质具有重要意义。对22种氯代芳烃化合物进行DFT-B3LYP/6-311G**水平全优化计算。据所得量子化学参数建立氯代芳烃对戈卑鱼急毒性(-IgLC₅₀)的QSAR模型。对训练集样本经逐步多元回归分析后,所建QSAR模型的复相关系数R²及去一法（LOO）交互检验复相关系数R²_cv分别为0.963和0.962,用预测集样本进行了外部预测,所得外部预测样本复相关系数R²_ext和外部预测集交互检验Q²_ext分别为0.968和0.820,表明所建立的QSAR方程具有较好的稳定性和预测能力。模型结果表明:氯代芳烃化合物的毒性与分子总能量（E_γ）、分子体积（V）及分子次最低空轨道能(E_NLUMO)的相关性较好。     

柴油低温流动改进剂的分子结构与降滤性能的关系研究

利用定量构效关系(QSAR)研究方法,从反映柴油结晶过程能量变化的参数中,筛选出其中对低温流动改进剂(CFI)降滤性能影响最关键的能量参数E_(inter-CFI)和E_(ads-C18(111)),并构建了描述E_(inter-CFI)、E_(ads-C18(111))与CFI降滤性能关系的方程。用同样方法筛选了对E_(inter-CFI)和E_(ads-C18(111))影响最关键的CFI分子结构特征参数,并分别构建了CFI分子结构特征参数与E_(inter-CFI)和E_(ads-C18(111))的关系方程。通过对方程的分析,详细阐述了E_(inter-CFI)和E_(ads-C18(111))影响降滤性能的内在机制,并详述了CFI分子的关键结构因素对柴油中蜡晶的生长形貌可能产生的影响,以及这些影响与降滤性能之间的关系。     

3D QSAR Studies,Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors

Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²_ncv = 0.988, r²_pred = 0.658; CoMSIA: q² = 0.843, r²_ncv = 0.989, r²_pred = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.     

Formation of Oxamic Acid During Drinking Water Treatment

Although oxamic acid has been identified as an ozone oxidation product from several precursor compounds, concentrations for drinking water have not been published previously. This study shows results from a full-scale drinking water treatment plant, noting that the mean concentrations for oxamic acid reached 21.3 μg/L after ozonation and prior to filtration. Subsequent multiple-layer filtration removed 85% of oxamic acid on average, and mean concentrations in drinking water were 2 μg/L. Up to 5.9% of the oxamic acid found in ozone-treated groundwater may be formed from Chloridazon metabolites.