Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors

Summary Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(1/K_i), with an explained variance r² of 0.885, and a cross-validated q² of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q² for one component was 0.62 and 0.61, respectively, while r² was 0.674. We demonstrate that the predictive r² based on the mean activity (Y_m) of the training set is misleading, while the test set Y_m-based predictive r² index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substitutents, and a smaller one at the C6-beta region.     

Applications of rule-induction in the derivation of quantitative structure-activity relationships

Summary Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable.The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness.Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed.The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.     

Comparison of different partial least-squares methods in quantitative structure-activity relationships

In this paper, the partial least-squares (PLS) is discussed. A new hybrid method combining PLS with GAGP, in which selection of variables, selection of functions and optimization of parameters were carried at the same time without any foreknowledge, was studied. A number of PLS algorithms (linear PLS, QPLS, SPL-PLS, NPLSNGA) that have appeared were compared from a theoretical viewpoint. Eight practical results with all the compared methods indicated that nonlinear models are better than linear model. In nonlinear methods, GAGP-PLS is significant.     

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n= 240, q2 = 0.65), while a 4-component model was selected for rlDHFR (n= 237, q2 = 0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2 = 0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2 = 0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.     

Fuzzy adaptive least squares and its use in quantitative structure-activity relationships

Fuzzy adaptive least squares (FALS), a pattern recognition method designed to correlate molecular structure with activity rating, has been developed. A novel feature of FALS is that the degree to which each sample belongs to an activity class is given using a membership function. The algorithm involves an iterative modification of forcing factors to maximize the sum of the membership function values over all samples. This paper first describes the method and calculation procedure of FALS89 (1989 version of FALS), and then shows its application to the correlation of structure with a potency rating of anticarcinogenic mitomycin derivatives and arginine-vasopressin antagonists. FALS89 applied to these samples showed considerably high reliability in both recognition and leave-one-out prediction.     

C(4)取代紫杉醇类似物的定量构效关系研究

用量子化学B3LYP／6-31G方法计算了23个C（4）取代紫杉醇类似物的结构,用遗传算法（GFA）对能量、电性、拓扑及热力学等类型的278个结构描述符进行筛选,并回归建立其抑制人体结肠癌细胞HCT-116活性的定量构效关系（QSAR）．QSAR方程含分子体积Vm、分子分支度指数CHI-O、分子中带正电荷原子的溶剂可积面积与其所带电荷之积的加和值Jurs-PPSA-3以及分子表面积S4个结构描述符．方程的拟合相关系数的平方R^0及交叉验证系数Q^2分别为0．956和0．913,所得QSAR具有可信的预报能力．由优化后的几何构型知,C（4）取代基、C（13）侧链和2-OBz三基团共同形成疏水腔,C（4）取代基的改变影响C（13）侧链的电子结构．C（13）连接的18号O原子的负电荷越大、3’位连接的NHBz基团的极性越小活性越高;C（4）取代基若为吸电子基对活性不利;适当增大分子体积、表面积和疏水性,保持一定的分支度对活性有利．     

新型2-杂环芳基苯并二氢吡喃-4-酮衍生物的三维定量构效关系研究

为开发新型环境友好杀菌剂，选择了黄烷酮类植物抗毒素为先导，利用生物等排取代原理对其结构进行修饰，设计合成了23个未见文献报道的2-杂环芳基苯并二氢吡喃-4-酮类化合物，并系统测定了所有化合物对水稻稻瘟病抑制活性的IC~5~0值。在此基础上，采用CoMFA方法对它们进行了三维定量结构活性关系研究，系统考查了网格结构、探针以及场类型对CoMFA结果的影响。研究结果表明，苯并二氢吡喃-4-酮苯环5,6,7位上应该引入一些体积较小且具有强供电子能力的取代基有利于提高化合物的杀菌活性。CoMFA分析对7位氧原子和19位氧原子的电性要求与前文采用Hansch-Fujita方法的分析结果是相一致的。     

Fuzzy structure-activity relationships

While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.     

Fuzzy structure-activity relationships

While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.     

Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity

The predictive accuracy of the model is of the most concern for computational chemists in quantitative structure-activity relationship (QSAR) investigations. It is hypothesized that the model based on analogical chemicals will exhibit better predictive performance than that derived from diverse compounds. This paper develops a novel scheme called "clustering first, and then modeling" to build local QSAR models for the subsets resulted from clustering of the training set according to structural similarity. For validation and prediction, the validation set and test set were first classified into the corresponding subsets just as those of the training set, and then the prediction was performed by the relevant local model for each subset. This approach was validated on two independent data sets by local modeling and prediction of the baseline toxicity for the fathead minnow. In this process, hierarchical clustering was employed for cluster analysis, k-nearest neighbor for classification, and partial least squares for the model generation. The statistical results indicated that the predictive performances of the local models based on the subsets were much superior to those of the global model based on the whole training set, which was consistent with the hypothesis. This approach proposed here is promising for extension to QSAR modeling for various physicochemical properties, biological activities, and toxicities.     

Holographic quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether congeners

Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the limited experimental data, it is important and necessary to develop structure-based models for prediction of the toxicity of the compounds. In this study, a new molecular structure representation, molecular hologram, was employed to investigate the quantitative relationship between toxicity and molecular structures for 18 PBDEs. The model with the significant correlation and robustness (r ² = 0.991, q ² _LOO = 0.917) was developed. To verify the robustness and prediction capacity of the derived model, 14 PBDEs were randomly selected from the database as the training set, while the rest were used as the test set. The results generated under the same modeling conditions as the optimal model are as follows: r ² = 0.988, q ² _LOO = 0.598, r ² _pred = 0.955, and RMSE (root-mean-square of errors) = 0.155, suggesting the excellent ability of the derived model to predict the toxicity of PBDEs. Furthermore, the structural features and molecular mechanism related to the toxicity of PBDEs were explored using HQSAR color coding.