Three-dimensional quantitative structure-activity relationship analysis of the new potent sulfonylureas using comparative molecular similarity indices analysis

The present study describes the implementation of a new three-dimensional quantitative structure-activity relationship (3D-QSAR) technique: comparative molecular similarity indices analysis (CoMSIA) to a set of novel herbicidal sulfonylureas targeted acetolactate synthase. Field expressions in terms of similarity indices in CoMSIA were applied instead of the usually used Lennard-Jones and Coulomb-type potentials in CoMFA. Two different kinds of alignment techniques including field-fit alignment and atom-by-atom fits were used to produce the molecular aggregate. The results indicated that those two alignment rules generated comparative 3D-QSAR models with similar statistical significance. However, from the predictive ability of the test set, the models from the alignment after maximal steric and electrostatic optimization were slightly better than those from the simple atom-by-atom fits. Moreover, systematic variations of some parameters in CoMSIA were performed to search the best 3D-QSAR model. A significant cross-validated q2 was obtained, indicating the predictive potential of the model for the untested compounds; meanwhile the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The CoMSIA coefficient contour plots identified several key features explaining the wide range of activities, which were very valuable for us in tracing the properties that really matter and getting insight into the potential mechanisms of the intermolecular interactions between inhibitor and receptor, especially with respect to the design of new compounds.     

Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., "active" versus "inactive"), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of the estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.     

The structure-activity relationship of inhibitors of serotonin uptake and receptor binding

Summary An analysis of five different datasets of inhibitors of serotonin uptake has yielded quantitative structure/ activity relationships (QSARs) which delineate the role of steric and hydrophobic properties essential for inhibition by phenylethylamine-type analogues.     

Regio- and stereoselective synthesis of truncated 3'-aminocarbanucleosides and their binding affinity at the A3 adenosine receptor

The stereoselective synthesis of truncated 3'-aminocarbanucleosides 4a-d via a stereo- and regioselective conversion of a diol 9 to bromoacetate 11a and their binding affinity towards the human A(3) adenosine receptor are described.     

Three-dimensional quantitative structure-activity relationship of 4,5,6,7-tetrahydrothienopyridines analogues as glucose-6-phosphatase inhibitors

A Three-Dimensional Quantitative Structure-activity Relationship (3D-QSAR) model that correlates the biological activities with the chemical structures of a series of Glucose-6-phosphatase inhibitors, exemplified by the 4,5,6,7-tetrahydrothienopyridines derivatives, was established by means of comparative molecular field analysis (CoMFA). The resulting leave-one-out cross-validated value (q²=0.600) and non-cross-validated value (r²=0.956) indicate that the obtained pharmacophore model indeed mimics the steric and electrostatic environment, where inhibitors bind to the enzyme. Furthermore, the developed model also possesses promising predictive ability as discerned by the testing on the external test set. The analysis of the CoMFA contour map, which reveal how steric and electrostatic interactions contribute to inhibitors' bioactivities, provide us with the important information to understand the molecular nature of inhibitor-enzyme interactions and to aid in the design of more potent Glucose-6-phosphatase inhibitors.     

香水百合香气成分的气相色谱保留指数三维定量构效关系研究

采用比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）方法,研究了香水百合中38种香气成分分子结构与气相色谱保留指数值之间的定量构效关系。用外部测试集验证法和留一交叉验证法对模型的稳健性和预测能力进行了检验,并通过CoMSIA模型和CoMFA模型的分子场三维等势图研究了这些化合物分子中不同化学结构对保留指数值的影响。检验结果表明,所建立的CoMSIA模型和CoMFA模型都具有较好的稳健性和预测能力,且能够合理解释结构对保留指数值的影响,可应用于对香水百合香气成分的色谱保留指数值的预测。与CoMFA模型相比,CoMSIA模型的预测准确度更高,在香水百合香气成分的色谱定量构效关系研究中,显然有更好的应用前景。     

Three-dimensional quantitative structure-activity relationship (3D QSAR) and pharmacophore elucidation of tetrahydropyran derivatives as serotonin and norepinephrine transporter inhibitors

Three-dimensional quantitative structure-activity relationship (3D QSAR) using comparative molecular field analysis (CoMFA) was performed on a series of substituted tetrahydropyran (THP) derivatives possessing serotonin (SERT) and norepinephrine (NET) transporter inhibitory activities. The study aimed to rationalize the potency of these inhibitors for SERT and NET as well as the observed selectivity differences for NET over SERT. The dataset consisted of 29 molecules, of which 23 molecules were used as the training set for deriving CoMFA models for SERT and NET uptake inhibitory activities. Superimpositions were performed using atom-based fitting and 3-point pharmacophore-based alignment. Two charge calculation methods, Gasteiger-Hückel and semiempirical PM3, were tried. Both alignment methods were analyzed in terms of their predictive abilities and produced comparable results with high internal and external predictivities. The models obtained using the 3-point pharmacophore-based alignment outperformed the models with atom-based fitting in terms of relevant statistics and interpretability of the generated contour maps. Steric fields dominated electrostatic fields in terms of contribution. The selectivity analysis (NET over SERT), though yielded models with good internal predictivity, showed very poor external test set predictions. The analysis was repeated with 24 molecules after systematically excluding so-called outliers (5 out of 29) from the model derivation process. The resulting CoMFA model using the atom-based fitting exhibited good statistics and was able to explain most of the selectivity (NET over SERT)-discriminating factors. The presence of −OH substituent on the THP ring was found to be one of the most important factors governing the NET selectivity over SERT. Thus, a 4-point NET-selective pharmacophore, after introducing this newly found H-bond donor/acceptor feature in addition to the initial 3-point pharmacophore, was proposed.     

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.     

Three-dimensional quantitative structure-activity relationship (QSAR) and receptor mapping of cytochrome P-450(14 alpha DM) inhibiting azole antifungal agents

Molecular modeling was performed by a combined use of conformational analysis and 3D-QSAR methods to distinguish structural attributes common to a series of azole antifungal agents. Apex-3D program was used to recognize the common biophoric structural patterns of 13 diverse sets of azole antifungal compounds demonstrating different magnitudes of biological activity. Apex-3D identified three common biophoric features significant for activity: N1 atom of azole ring, the aromatic ring centroid 1, and aromatic ring centroid 2. A common biophore model proposed from the Apex-3D analysis can be useful for the design of novel cytochrome P-450(14 alpha DM) inhibiting antifungal agents.     

A new synthetic route to (North)-methanocarba nucleosides designed as A3 adenosine receptor agonists

Activation of the A3 adenosine receptor (AR) is associated with cerebroprotective, cardioprotective, and anticancer effects. Among potent and selective A3 AR agonists are novel methanocarba adenosine analogues in which the conformation of a pseudo-ribose moiety is locked in the North (N) hemisphere of the pseudorotational cycle. 5'-Uronamide (N)-methanocarba nucleosides, such as MRS1898 and MRS2346, are examples of full agonists of the human A3 AR. An improved convergent approach from easily accessible 2,3-O-isopropylidene-d-erythrose (2b), and the combination of a strategic intramolecular cyclopropanation step plus the acid-catalyzed isomerization of an isopropylidene group, provided a suitable pseudosugar precursor (23) for the synthesis of MRS1898, MRS2346, and related analogues. This new synthetic route uses readily available building blocks and opens the way for the preparation of a variety of targets on a reasonable scale.     

Three-dimensional structure-activity relationship analysis between motilin and motilide using conformational analysis and a novel molecular superposing method

Motilide, an erythromycin derivative, has been shown to equal activity to that of motilin as an agonist at the motilin receptor. However, there is little information on the three-dimensional (3D) structure-activity relationship between these two molecules, largely because they have quite different structures. In this study, we applied a rational computational procedure consisting of conformational analysis and a novel superposing method to investigate the 3D structure-activity relationship between motilide and motilin. We propose common 3D structural features between these molecules, which may be important for their similar activity.     

A direct method for the alkylation of adenosine nucleosides at position 8.

Alkylation of adenosine derivatives at carbon 8 can be conveniently achieved by lithiation followed by reaction with an alkyl halide.     

A comparative study of quantitative structure-activity relationship methods based on gallic acid derivatives

By using hologram quantitative structure-activity relationship (HQSAR) and comparative molecular field analysis (CoMFA) methods, the relationships between the structures of 49 gallic acid derivatives and their analgesic activity have been investigated to yield statistically reliable models with considerable predictive power. The best HQSAR model was generated using atoms, bond and connectivity as fragment distinction parameters and fragment size 5-7 from a hologram length of 307 with 3 components. High conventional r2 (r2 = 0.825) and cross-validation r2 (r2(cv) = 0.726) values were obtained. CoMFA analyses varying lattice size and location, grid spacing, probe charges and using, Tripos standard and Indicator force field were performed. The best model was developed with 4 components using sp3-hybridized carbon atom with +1.0 charge as probe, grid spacing (2 A), lattice offset (1.0, 3.0, -2.5). The CoMFA model showed a conventional correlation coefficient r2 of 0.889 and across-validation r2(cv) equals to 0.633. The robustness and predictive ability of the HQSAR and CoMFA models have been validated by means of an external test set. The results indicate that both models possess high statistical quality in the prediction of analgesic potency of novel gallic acid analogs.