Modeling robust QSAR. 2. iterative variable elimination schemes for CoMSA: application for modeling benzoic acid pKa values

A variety of issues decide the efficiency of 3D QSAR methods, and their practical importance for drug design is still controversial. This refers both to the predictive ability and the possibility for the indication of these areas within 3D molecular representations that are responsible for biological or chemical effects. Technically, the latter comes down to the selection or elimination of the reliable variables during 3D QSAR modeling using the Partial Least-Squares (PLS) method. In this paper we used a series of benzoic acids to test the dependence between the predictive ability and variable selection performance of PLS with Iterative Variable Elimination (IVE-PLS) in the Comparative Molecular Surface Analysis (CoMSA) modeling of Hammett constant which correlates with the pKa values. Modeling this chemical effect allowed us to select the IVE-PLS variant that plots the contour maps indicating a carboxylic function, i.e., the region including the dissociation reaction center that determines the respective pKa values. In fact, it appeared that a novel robust IVE version is capable of the indication of the proper contour plots independent of the method used for the calculation of partial atomic charges (AM1 or Gasteiger-Marsili).     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

QSAR study of angiotensin II antagonists using robust boosting partial least squares regression

In the current study, robust boosting partial least squares (RBPLS) regression has been proposed to model the activities of a series of 4H-1,2,4-triazoles as angiotensin II antagonists. RBPLS works by sequentially employing PLS method to the robustly reweighted versions of the training compounds, and then combing these resulting predictors through weighted median. In PLS modeling, an F-statistic has been introduced to automatically determine the number of PLS components. The results obtained by RBPLS have been compared to those by boosting partial least squares (BPLS) repression and partial least squares (PLS) regression, showing the good performance of RBPLS in improving the QSAR modeling. In addition, the interaction of angiotensin II antagonists is a complex one, including topological, spatial, thermodynamic and electronic effects.     

基于多元线性回归的血管紧张素转化酶抑制肽定量构效关系建模研究

利用氨基酸结构描述符SVHEHS分别对血管紧张素转化酶(Angiotensin I-converting Enzyme,ACE)竞争性抑制二肽、三肽、四肽序列表征后,建立结构与活性的多元线性回归(MLR)模型。ACE抑制二肽模型的相关系数、交叉验证相关系数、均方根误差、外部验证相关系数分别为0.851、0.781、0.327、0.792;三肽模型分别为0.805、0.717、0.339、0.817;四肽模型分别为0.792、0.553、0.393、0.630。研究表明,运用该描述符建立的ACE抑制肽MLR模型拟合、预测能力均较好,能较好解释ACE抑制肽的活性与结构间的关系。     

Modeling robust QSAR 3: SOM-4D-QSAR with iterative variable elimination IVE-PLS: application to steroid, azo dye, and benzoic acid series

In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.     

Modeling based on subspace orthogonal projections for QSAR and QSPR research

A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte‐Carlo cross‐validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd.     

基于逐步非线性回归的血管紧张素转化酶抑制肽QSAR建模

线性特征选择方法可提升定量构效关系(QSAR)模型的预测能力,但易忽略特征(理化属性)与分子活性间的非线性关系。本文提出基于支持向量回归(SVR)的逐步非线性回归(SSNR)特征选择算法并用于降血压药物血管紧张素转化酶(ACE)抑制肽的QSAR研究。首先以具有不同背景的5组分子描述符分别表征肽序列,以SSNR实施特征选择,再通过智能一致性模型(ICM)对各组描述符对应子模型的预测活性进行加权整合,获得最终活性预测值。在ACE抑制二肽与三肽两个数据上的应用结果表明,SSNR获得的特征子集结合ICM策略可有效提升模型预测能力(二肽的平均Q■为0.675±0.002,三肽为0.663±0.013),优于遗传算法-偏最小二乘(0.538±0.049、0.599±0.047)与逐步线性回归(0.583±0.041、0.675±0.010)。最后基于抑制活性已知肽序列预测所有活性未知肽的活性,分析了高活性肽及其氨基酸偏好性,为人工合成潜在高活性ACE抑制肽提供可能的序列组合。     

基于不同建模方法的ACE抑制肽QSAR比较研究

以自组建的血管紧张素转化酶(Angiotensin I-converting enzyme)抑制肽库为研究对象,采用氨基酸描述符SVHEHS(Scores vector of hydrophobic,electronic,hydrogen bonds and steric properties)对各肽样本进行结构表征后,进行自交叉协方差(Auto cross covariances,ACC)处理,并分别利用多元线性回归(Multiple linear regression,MLR)、偏最小二乘(Partial least square regression,PLS)、人工神经网络(Artificial neural networks,ANN)3种建模方法进行ACE抑制肽QSAR建模。结果显示,所得MLR、PLS与ANN模型的相关系数(Correlation coefficient,R2)分别为0.744、0.862、0.958,留一交叉验证相关系数(Leave-one-out cross-validated correlation coefficient,Q2LOO)分别为0.532、0.829、0.948,外部验证复相关系数(External validated correlation coefficient,Q2ext)分别为0.567、0.632、0.634。因此,SVHEHS结合上述3种建模方法均适用于ACE抑制肽的QSAR研究,其中ANN的建模效果最优。     

Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.     

A Novel Electron-Conformational Approach to Molecular Modeling for QSAR by Identification of Pharmacophore and Anti-Pharmacophore Shielding

Abstract

A novel method of pharmacophore identification and activity prediction in structure-activity (structure-property) relationships is worked out as an essential extension and improvement of previous publications. In this method each conformation of the molecular systems in the training set of the SAR problem is presented by both electronic structure and geometry parameters arranged in a matrix form. Multiple comparisons of these matrices for the active and inactive compounds allows one to separate a smaller number of matrix elements that are common for all the active compounds and are not present in the same arrangement in the inactive ones. This submatrix of activity represents the pharmacophore (Pha).

By introducing the Anti-Pharmacophore Shielding (APS) defined as molecular groups and competing charges outside the Pha that hinder the proper docking of the Pha with the bioreceptor, the procedure of Pha identification is essentially reduced to the treatment of a smaller number of simplest in structure most active and inactive compounds. A simple empirical scheme is suggested to estimate the APS numerically, while the contributions of different conformations of the same compound are taken into account by means of Boltzmann distribution. This enables us to make approximate quantitative predictions of activities.

In application to rice blast activity we reached an approximately 100% (within experimental error) prediction probability of the activity qualitatively (yes, no), and with r ² = 70% quantitatively.     

Predictivity of QSAR

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.