Internal symmetry groups of non-rigid molecules

Hougen has established, for quasi-rigid molecules, the relationship between permutationinversions acting on the molecular Hamiltonians written in Cartesian co-ordinates and permutation-rotations (perrotations) of symmetry acting on nuclear equilibrium configurations. We extend these relations to the case of non-rigid molecules. For this, we introduce kinetic perrotations which act on nuclear equilibrium configurations in the same way as do Altmann's isodynamic operators. We show that isodynamic operators do not always form a group. Moreover, their action cannot be extended to the electrons. They cannot be used for the classification of molecular wave functions. This classification is achieved by using the group of Longuet-Higgins and the group of the corresponding feasible perrotations.     

Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm

Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model’s performance, support vector machine and Naïve Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.     

Spectroscopic QSAR methods and self-organizing molecular field analysis for relating molecular structure and estrogenic activity

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.     

ALS抑制剂合理分子设计的研究进展

以乙酰乳酸合成酶为靶标合理设计开发新型超高效除草剂是当前除草剂化学研究中的重要领域。结合本课题组的研究工作,从ALS抑制剂的结构特征、分子力学与量子化学研究、定量构效关系(QSAR)与三维定量构效关系(3D-QSAR)研究、非线性QSAR研究以及新型除草剂的分子设计等几个方面对该领域的研究现状进行了总结报道,并对该领域的发展前景及存在的问题进行了展望。     

Joint QSAR analysis using the Free-Wilson approach and quantum chemical parameters

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (Sw), 1-octanol/water partition coefficient (Kow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC50) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a bio-chemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.     

QSAR modeling of large heterogeneous sets of molecules

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 noncongeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

Comparative QSAR: Radical Toxicity and Scavenging. Two Different Sides of the Same Coin

Abstract

From an analysis of the toxicity of phenols to rat embryos and anilines to embryo fibroblast cells a new type of toxicity is postulated for these classes of compounds. Substituents which increase the electron density on the aromatic ring as estimated by σ⁺ or ε_HOMO increase potency. It is postulated that it is the radical form of the phenols and the anilines that accounts for their toxicity. The results are compared with QSAR for radical scavengers and oxidoreductases acting on phenols, anilines and carbazoles.     

Determining the validity of a QSAR model--a classification approach

The determination of the validity of a QSAR model when applied to new compounds is an important concern in the field of QSAR and QSPR modeling. Various scoring techniques can be applied to specific types of models. We present a technique with which we can state whether a new compound will be well predicted by a previously built QSAR model. In this study we focus on linear regression models only, though the technique is general and could also be applied to other types of quantitative models. Our technique is based on a classification method that divides regression residuals from a previously generated model into a good class and bad class and then builds a classifier based on this division. The trained classifier is then used to determine the class of the residual for a new compound. We investigated the performance of a variety of classifiers, both linear and nonlinear. The technique was tested on two data sets from the literature and a hand built data set. The data sets selected covered both physical and biological properties and also presented the methodology with quantitative regression models of varying quality. The results indicate that this technique can determine whether a new compound will be well or poorly predicted with weighted success rates ranging from 73% to 94% for the best classifier.