QSAR of caffeines by similarity cluster prediction

A novel QSAR approach based on correlation weighting and alignment over a hypermolecule that mimics the investigated correlational space was performed on a set of 40 caffeines downloaded from the PubChem database. The best models describing log P and LD50 values of this set of caffeine derivatives were validated against the external test set and in a new predictive model by using clusters of similarity.      

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.     

Molecular similarity studies on sandalwood odour molecules: Investigations of the conformational space

Summary Odour differences of some campholene and fencholene derivatives are explained by the analysis of the conformational space and the molecular shape of these molecules. The high flexibility caused by free rotation of some carbon-carbon bonds leads in one case to a large number of energetically possible conformations which have to be taken into account for a study of molecular similarity. In another case, steric restrictions reduce the number of relevant conformations such that no active conformation exists in a thermodynamic equilibrium.Conformational Calculations on Sandalwood Odour XII; for part XI, see Ref. [1]     

Some PhCNO-containing molecules; mass spectral similarity and molecular diversity

Some diverse compounds possessing a PhC?NO unit cleave the N? O link upon electron impact to give [PhCN]⁺˙. Different and especially significant modes of N? O scission occur in metastable processes when the oxygen atom of the PhCNO group is exocyclic to a heterocyclic nitrogen atom. Upon electron impact, rupture of the N? O bond in PhCNO-containing molecules generally dominates over 1,3-dipolar cycloreversion, which generates the radical cation of benzonitrile oxide. Stable PhCNO-containing molecules survive competing fragmentations in the ion source to produce [M+H]⁺ ions of moderate relative intensity. Other ions, which are larger than [M]⁺ are implicated where M is PhCH?NOR. Several values of m/z for metastable ions are common to compounds which have a PhCNO moiety. These m/z values generally derive both from a parent ion with m/z?105 and from a PhC?NO moiety.     

Design and synthesis of anti-MRSA benzimidazolylbenzene-sulfonamides. QSAR studies for prediction of antibacterial activity

A series of benzimidazolylbenzenesulfonamide compounds containing electron-releasing and electron-withdrawing substituents were synthesized and tested for their in vitro antibacterial activity. Two BZS compounds showed strong antibacterial activity against methicillin-resistant Staphylococcus aureus and Bacillus subtilis. Quantitative studies of their structure-activity relationship using a simple linear regression analysis were applied to explore the correlation between the biological activity and the charges on acidic hydrogen atoms in the synthesized compounds.     

The quality of QSAR models: problems and solutions

Assessment of the quality of goodness-of-fit and the confidence in predictivity (prediction power) are the main terms used to define the statistical quality of QSAR models. Three parts of this assessment can be defined as: (1) Measure of goodness-of-fit. (2) Validation of model stability. (3) Predictivity analysis. Currently there are no mandatory requirements for the validation methods to be used and rules for the quantitative confidence estimates. To compare the statistical quality of QSAR models it is necessary to have an overall statistical quality index which will depend on the goodness-of-fit, validation and predictivity results together. To do so it is necessary to define the set of mandatory parameters for all three parts of assessment listed above and develop the approach for overall quality estimates based on these parameters. It is also necessary to include into the overall index the penalty mechanism for parameter absence. The goal of the present study is to analyse parameters for all three parts of the QSAR model statistical quality assessment and investigate the flexible weighting approach for the overall statistical quality index development. Due the different statistical parameters traditionally used for assessment of goodness-of-fit it is necessary to create the mechanism, which allows flexible set of parameters to be used for the overall statistical quality index. Only after approval by scientific community and regulatory boards the final set of mandatory parameters can be selected.     

Refinement and use of the approximate similarity in QSAR models for benzodiazepine receptor ligands

Several considerations for refining the approximate similarity measurements have been introduced in this paper: the use of topological invariants for the calculation of similarity indexes and the development of new similarity correction processes. The quality of the new similarity measurements obtained with the proposed methods has permitted the development of fast, cheap, and simple quantitative structure-activity relationship models for the prediction of biological activities of nonbenzodiazepine gamma-aminobutyric acid(A)/benzodiazepine receptor ligands (58 compounds). Internal and external validations were carried out for the approximate similarity matrices computed using different approaches. Satisfactory results which compare reasonably well with a 3D approach were obtained: Q2= 0.65 and standard error in cross validation SECV= 0.83 for the training stage; r = 0.79 and error in external prediction = 0.82 for the test step. In addition, the method proposed was compared with other topological approaches based on constitutional similarity and on fingerprints. Satisfactory results were obtained.     

苯胺衍生物式电位的QSAR研究

以AM1量子化学半经验分子轨道方法计算22个苯胺衍生物的分子结构参数,用多元线性回归的方法研究了13个式电位已知的苯胺衍生物的氧化式电位与其分子结构参数之间的关系.苯胺衍生物式电位与离子化电势(Ip),N原子上的净电荷(e(N),总的偶极矩(μt)和第四个碳上的净电荷(e(C4))有很好的相关性,回归方程为:E0′=-7.820 5 0.884 9(Ip-3.739(e(N)-0.093 56μt 0.925 1(e(C4)),(R=0.979,SD为0.023 6).依据回归方程合理地预测了其余9个苯胺衍生物的离子化电势和式电位,探讨了取代基性质和取代位置对离子化电势和式电位的影响.