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.     

Combinatorial library synthesis of antioxidant compounds

Since oxidative cellular damage contributes to the development of cancers, heart disease and ageing, the synthesis of antioxidative agents which are able to either prevent or mitigate oxidative stress to cells is an important area of investigation. Combinatorial chemistry has had a profound impact on the discovery and optimisation of potential lead compounds, especially in the medicinal field. This review details recent examples of combinatorial chemistry dealing with the synthesis of novel antioxidants with an emphasis on solid phase compound synthesis and parallel library synthesis.     

Chemometric QSAR studies of antifungal azoxy compounds

Summary Quantitative structure-activity relationships (QSARs) for 16 azoxy compounds with antifungal activity have been studied by the combined approach of a partial least-squares method and factorial design. The PLS model equation suggested the structural requirements of two substituents, R₁ and R₂, for the antifungal activity. The sterically bulky and hydrophobic R₁ substituents and electron-withdrawing R₂ substituents are favorable for the activity. We propose candidate compounds which are more potent than the compounds based on QSAR data. In this study, we show that the chemometric approach is a powerful tool for QSAR studies and drug design.Abbreviations PLS partial least squares - FD factorial design - MLR multiple linear regression - PPs principal properties     

Multi-KNN-SVR组合预测在含氟化合物QSAR研究中的应用

为深入认识含氟农药生物活性与其结构之间的关系, 建立了理想的QSAR模型, 从化合物油水分配系数等7个分子结构描述符出发, 基于支持向量回归(SVR)和MSE最小原则, 经自动寻找最优核函数和非线性筛选描述符, 构建了多个K-最近邻(KNN)预测子模型. 再经非线性筛选获得保留子模型, 以保留子模型实施组合预测(Multi-KNN-SVR). 33种含氟化合物对5种不同病害生物活性的留一法组合预测结果表明, 采用非线性筛选描述符和KNN子模型能有效地提高预测精度, 基于多个KNN子模型的非线性组合能进一步提高预测性能. Multi-KNN-SVR组合预测在QSAR以及其它相关预测研究中具有广泛应用前景.     

Combinatorial libraries of bis-heterocyclic compounds with skeletal diversity

Combinatorial solid-phase synthesis of bis-heterocyclic compounds, characterized by the presence of two heterocyclic cores connected by a spacer of variable length/structure, provided structurally heterogeneous libraries with skeletal diversity. Both heterocyclic rings were assembled on resin in a combinatorial fashion.     

QSAR modeling and chemical space analysis of antimalarial compounds

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.     

Quantum topological molecular descriptors in QSAR analysis of organophosphorus compounds

A Quantitative structure–activity relationship study is performed on a set of organophosphorus compounds to reveal structural and quantum‐chemical features influencing the toxic effect. The properties derived from the topological analysis of the electron density have been used to model the toxicity data. A multiple linear regression analysis in conjunction with genetic algorithm is used in the study, followed by subsequent validation of the results. Obtained QSAR models are beneficial for virtual screening of toxicity for new compounds of interest. Because toxicity of organophosphorus compounds is dependent on conformational properties, a conformational search has been performed before optimization of geometries. All quantum‐chemical calculations are carried out at DFT/B3LYP level of theory with 6‐311++G(d,p) basis set. Frequency calculations are performed after full geometry optimization. Ab initio wave functions were obtained for further analysis and evaluation of quantum topological properties of target molecules. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2012     

Combinatorial enantiomeric separation of diverse compounds using capillary array electrophoresis

Combinatorial chiral separations were performed on a 96-capillary array electrophoresis system. A comprehensive enantioseparation protocol employing neutral and sulfated cyclodextrins as chiral selectors for common basic, neutral and acidic compounds was developed. By using only four judiciously chosen separation buffers, successful enantioseparations were achieved for 49 out of 54 test compounds spanning a large variety of pK and structures. Therefore, unknown compounds can be screened in this manner to identify the optimal enantioselective conditions in just one run.     

3D QSAR investigation of the blood-brain barrier penetration of chemical compounds

In the present study, we investigated structure-permeability relationships for the blood-brain barrier (BBB) of 16 imipramine and phenothiazine derivatives. The compounds belong to structurally related chemical classes of catamphiphiles, representatives of which have previously been investigated for membrane activity and ability to overcome multidrug resistance (MDR) in tumour cells. These studies show that phenothiazines and structurally related drugs (imipramines, thioxanthenes, acridines) interact with membrane phospholipids, and additionally inhibit the MDR transport P-glycoprotein. This study aimed to identify common 3D structural characteristics of these compounds related to their mechanism of transport across the BBB. For this purpose Genetic Algorithm Similarity Programme (GASP), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied. The results demonstrate the importance of the spatial distribution of molecular hydrophobicity for the BBB penetration of the investigated compounds. It suggests that the compounds should follow a specific profile of two hydrophobic and one hydrophilic centres in a particular space configuration, for optimal BBB penetration.     

QSAR and Mechanistic studies on the genotoxic compounds including environmental effects

The effects of a proximate condensed environment as the solvent and cellular structured patterns (biopolymers, membranes, etc.) play an important role in determination of the courses of molecular processes in biology. We present here the background of methods developed for such an environmental effects estimation combining the continuum and discrete models. Their applications within theoretical studies into the mechanisms of carcinogenic action of alkylating N-nitrosocompounds are shown. The results given cover four different areas, namely the quantitative structure-activity relationship, mechanistic studies into their metabolic activation reactions, interactions of the ultimate carcinogens with DNA, and finally their genetic consequences.     

一个新的拓扑指数用于有机化合物的QSPR/QSAR研究

在分子图的邻接矩阵和距离矩阵的基础上提出了一个新的拓扑指数Xu,该拓扑指数易于计算,对C~2-C~1~6饱和烷烃有较高的结构区分能力,通过适当的处理可方便地推广到含多重键杂原子体系。该指数与饱和烷烃的正常沸点等理化性质,不饱和链烃类化合物的热容以及某些脂肪醇的毒性和疏水性参数均具有较好的性质相关性。绝大多数理化性质与Xu指数均能建立简单线性模型,且相关系数均大于0.99,表明该指数有望在QSPR/QSAR研究中作为一个新的参数而获得推广应用。