QSAR and Pharmacophore Studies of Thiazolidine-4-carboxylic Acid Derivatives as Novel Influenza Neuraminidase Inhibitors Using HQSAR, Topomer CoMFA and CoMSIA

In order to understand the chemical-biological interactions governing their activities toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR (hologram quantitative structure activity relationship), Topomer CoMFA and CoMSIA (comparative molecular similarity indices analysis) models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients (R2) of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients (Q2) of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set (R2test) of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set (Q2ext) of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.     

Application of PCA and HCA to the Structure-activity Relationship （SAR） Study of Fluoroquinolones

1 INTRODUCTION The earliest antibacterial agents used as chemo- therapeutic drugs in the 1920s were synthetic agents, such as sulponamides. Since the discovery of peni- cillin and its entry into clinical use in the 1940s, the field has been dominated over the past 50 years by natural products or their semi-synthetic derivatives. The fluoroquinolones are the only synthetic anti- bacterial agents to rival β-lactams for impact in clinical usage in the antibacterial field. In recent years, …     

QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors

In order to understand the chemical-biological interactions governing their activities toward neuraminidase(NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. Here a quantitative structure activity relationship(QSAR) model was built by three-dimensional holographic atomic vector field(3 D-HoVAIF) and multiple linear regression(MLR). The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations. The correlation coefficient(R2) of established MLR model was 0.984, and the cross-validated correlation coefficient(Q2) of MLR model was 0.947. Furthermore, the cross-validated correlation coefficient for the test set(Qext2) was 0.967. The binding mode pattern of the compounds to the binding site of integrase enzyme was confirmed by docking studies. The results of present study indicated that this model can aid in designing more potent neuraminidase inhibitors.     

Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation

An unusually large data set of 397 piperazinyl-glutamate-pyridines/pyrimidines as potent orally bioavailable P2Y(12) antagonists for inhibition of platelet aggregation was studied for the first time based on the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) methods. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) studies have been performed with a training set of 317 compounds, estimating three superimposition methods. The best CoMFA and CoMSIA models, derived from superimposition I, shows leave-one-out cross-validation correlation coefficients (Q(2)) of 0.571 and 0.592 as well as the conventional correlation coefficients (R(2)(ncv)) of 0.814 and 0.834, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10-fold cross-validation, further indicate the highly statistical significance of the optimal models. The external predictive abilities of these models were evaluated using a prediction set of 80 compounds, producing the predicted correlation coefficients (R(2)(pred)) of 0.664 and 0.668, respectively. The key amino acid residues were identified by molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good concordance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the rational modification of molecules in order to design more potent P2Y(12) antagonists. We hope the developed models could provide some instructions for further synthesis of highly potent P2Y(12) antagonists.     

Comparative studies on some metrics for external validation of QSPR models

Quantitative structure-property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like Q2(F1), Q2(F2), Q2(F3), CCC, and r2(m) (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, r2(m) shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, r2(m) provides the most stringent criterion (especially with Δr2(m) at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes.     

Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.     

4D-QSAR analysis of a set of ecdysteroids and a comparison to CoMFA modeling.

The ecdysteroid-responsive Drosophila melanogaster B(II) cell line is a prototypical homologous inducible gene expression system. A training set of 71 ecdysteroids, for which the -log(EC(50)) potencies in the ecdysteroid-responsive B(II) cell line were measured, was used to construct 4D-QSAR models. Four nearly equivalent optimum 4D-QSAR models, for two modestly different alignments, were identified (Q(2) = 0.76-0.80). These four models, together with two CoMFA models, were used in consensus modeling to arrive at a three-dimensional pharmacophore. The C-2 and C-22 hydroxyls are identified as hydrogen-bond acceptor sites which enhance activity. A hydrophobic site near C-12 is consistent with increasing activity. The side-chain substituents at C-17 are predicted to adopt semiextended "active" conformations which could fit into a cylinder-shaped binding pocket lined largely with nonpolar residues for enhanced activity. A test set of 20 ecdysteroids was used to evaluate the QSAR models. Two 4D-QSAR models for one alignment were identified to be superior to the others based on having the smallest average residuals of prediction for the prediction set (0.69 and 1.13 -log[EC(50)] units). The correlation coefficients of the optimum 4D-QSAR models (R(2) = 0.87 and 0.88) are nearly the same as those of the best CoMFA model (R(2) = 0.92) determined for the same training set. However, the cross-validation correlation coefficient of the CoMFA model is less significant (Q(2) = 0.59) than those of the 4D-QSAR models (Q(2) = 0.80 and 0.80).     

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R² values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes.     

3-取代硫基-5-(2-羟基苯基)-4H-1,2,4-三唑类化合物抑菌活性的定量构效关系和结构修饰的理论研究

用DFT-B3LYP 方法, 在基组6-31G 水平, 对24 种3-取代硫基-5-(2-羟基苯基)-4H-1,2,4-三唑类化合物分子进行几何优化, 并计算了E_HOMO, E_LUMO, E_NHOMO, E_NLUMO, Q_C1～Q_C8, Q_N1～Q_N3, Q_O, Q_S和ΔE₁, ΔE₂, ΣQ 等量子化学描述符(q_c). 通过最佳变量子集回归建立13 种上述化合物对大肠杆菌、白色念珠菌、金黄色葡萄球菌等抑菌活性(A_J: A_e, A_m 和A_s) 的QSAR 模型. 对于大肠杆菌的A_e 模型的相关系数(R²)和逐一剔除法交叉验证系数R_cv² 依次为0.930 和0.871, 相应白色念珠菌A_m 模型为0.926 和0.869, A_s 模型为0.781 和0.572. 通过R_adj², F, R_cv², VIF, AIC, FIT 等检验, 上述模型具有令人满意的稳健性和预测能力. 结果显示ΔE₁ 和ΣQ 直接影响这些化合物的生物活性: ΣQ 增大, 其抑菌活性增强; ΔE₁ 越高, A_J 下降. 据此提出三唑类化合物分子可能的抑菌机理. 由此发现, 在三唑类化合物分子的R 中合适部位选用吸电子能力较强的取代基团进行结构修饰, 有利于提高被修饰后分子的抑菌活性. 根据对R 进行结构修饰(共提出11 种化合物), 得出4 种抑菌活性均超出100%的三唑类化合物(质量分数为0.01%), 希望将来得到生物实验的证实.     

电拓扑状态预测多氯二苯并噻吩及噻吩砜化合物的气相色谱保留指数

以原子类型电拓扑状态指数(ETSI)有效表征了135 个多氯二苯并噻吩(PCDT)和135 个多氯二苯并噻吩砜(PCDTO₂)的分子结构, 应用基于预测的变量选择与模型化(VSMP)方法建立PCDT 和PCDTO₂ 化合物在DB-5 气相色谱柱上的气相色谱保留指数(RI)与分子结构(ETSI)的定量相关模型, 模型的相关系数r² 分别为0.9939 和0.9729, LOO 交叉验证相关系数 q² 分别为0.9921 和0.9692. 为验证模型稳定性和预测能力, 应用17 个PCDT 和PCDTO₂ 训练集样本构建的QSRR 模型的r² 分别为0.9959 和0.9783, LOO 交叉验证相关系数 q² 分别为0.9921 和0.9740, 说明模型具有良好的稳定性. 以此模型预测外部8 个检验集及110 个预测集的RI 值, 8 个检验集样本的结果表明训练集模型具有良好预测能力.     

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.     

芳香族化合物生物降解性的QSBR研究

分别采用线性基团贡献法和人工神经网络法对芳香族化合物的生物降解最大去除率QTOD进行QSBR研究。得到不同基团对生物降解性的贡献顺序为 :C6H5>COOH >OH >CO >CH3 >C1 >NH2>NO2 。线性基团贡献法对于训练组和测试组的预测正确率分别为 86%和 80 % ,总的预测正确率达85 % ;而人工神经网络法的预测正确率分别为 94%、80 %和 92 %。结果表明 ,线性基团贡献法和神经网络法的预测效果均很好 ,而神经网络法的预测更精确。