Comparative Molecular Field Analysis Contour Research Articles

Three-dimensional quantitative structural-activity relationship and molecular dynamics study of multivariate substituted 4-oxyquinazoline HDAC6 inhibitors.

3D-QSAR models were established by collecting 46 multivariate-substituted 4-oxyquinazoline HDAC6 inhibitors. The relationship of molecular structure and inhibitory activity was studied by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). The results showed the models established by CoMFA (q2 = 0.590, r2 = 0.965) and CoMSIA (q2 = 0.594, r2 = 0.931) had good prediction ability. At the same time, 3D-QSAR models met the internal verification, external verification and AD test. Ten new compounds were designed based on CoMFA and CoMSIA contour maps and their pharmacokinetic/toxic properties (ADME/T) were evaluated. It was found that most compounds have well safety profile and pharmacokinetic property. Then, we explored the interaction between HDAC6 and compounds by molecular docking. The results showed that the binding mode of the new compounds with HDAC6 was the same as the template compound 46, and the hydrogen bond and hydrophobic bond played a vital role in the binding process. Molecular dynamics simulation results showed that residues Ser531, His574 and Tyr745 played key roles in the binding process. All newly designed compounds had lower energy gap and binding energy than compound 46 according to DFT analysis and free energy analysis. This study provided a theoretical reference for designing compounds of higher activity and a new idea for the development of novel HDAC6 inhibitors.

Development of 3D-QSAR models for predicting the activities of chemicals to stimulate muscle growth via β2-adrenoceptor

β2-adrenoceptor (β2AR) agonists can stimulate skeletal muscle growth. Their illegal use in food-producing animals, human athletes and bodybuilders causes adverse health effects. In the present study, we developed 3D-QSAR models for predicting the activity of chemicals which can stimulate skeletal muscle growth through β2AR. The activity of 25 β2AR agonists was measured by β2AR-cAMP response element (CRE) –luciferase (Luc) reporter assay. The 3D-QSAR models were built using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models displayed high external predictability (R2 0.996 and 0.992, respectively) and good statistical robustness, and revealed that electrostatic effects were the most prominent forces influencing the activity of β2AR agonists. The CoMFA and CoMSIA contour plots provided clues regarding the main chemical features responsible for the activity variations and also resulted in predictions which correlate very well with the observed activity. In vitro study with differentiated myotubes showed that the potency orders of β2AR agonists in activating the β2AR-CRE-Luc reporter and in upregulating CREB target genes related to muscle growth were consistent. These 3D-QSAR models provide tools for predicting the activity of chemicals which might be illegally used in livestock or humans to stimulate skeletal muscle growth.

Discovery of 2-(3,4-dialkoxyphenyl)-2-(substituted pyridazin-3-yl)acetonitriles as phosphodiesterase 4 inhibitors with anti-neuroinflammation potential based on three-dimensional quantitative structure-activity relationship study.

Phosphodiesterase 4 (PDE4) inhibitors with potential activities for CNS disorders provide a new therapeutic strategy for depression. To discover PDE4 inhibitors with anti-neuroinflammation activities, reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models on our previous reported catecholic PDE4 inhibitors was built with a statistically significant cross-validated coefficient (q2 ), conventional coefficient (r2 ), and good predictive capabilities based on the molecular docking results, using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods. Based on the analysis of CoMFA and CoMSIA contour maps, a series of 2-(3,4-dialkoxyphenyl)-2-(substituted pyridazin-3-yl) acetonitriles 16a-i was designed and synthesized. Among these compounds, compound 16a exhibited good inhibitory activities toward PDE4B1 and PDE4D7 with mid-nanomolar IC50 values and potential anti-neuroinflammation activity in BV-2 cells. Docking simulation of compound 16a in the PDE4 catalytic domain activity pocket revealed that compound 16a maybe assumed a "V-shaped" conformation, extending the side chain to S-pocket.

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

ABSTRACTIn this study, based on molecular docking analysis and comparative molecular field analysis (CoMFA) modelling of a series of 71 CD38 inhibitors including 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides, new CD38 inhibitors were designed. The interactions of the molecules with the greatest and the lowest activities with the nicotinamide mononucleotide (NMN) binding site were investigated by molecular docking analysis. A CoMFA model with four partial least squares regression (PLSR) components was developed to predict the CD38 inhibitory activity of the molecules. The r2 values for the training and test sets were 0.89 and 0.82, respectively. The Q2 values for leave-one-out cross-validation (LOO-CV) and leave-many-out cross-validation (LMO-CV) tests on the training set were 0.65 and 0.64, respectively. The CoMFA model was validated by calculating several statistical parameters. CoMFA contour maps were interpreted, and structural features that influence the CD38 inhibitory activity of molecules were determined. Finally, seven new CD38 inhibitors with greater activity with respect to the greatest active molecules were designed.

QSAR and Docking Studies of DATA Analogues as HIV-1 Reverse Transcriptase Inhibitors

Background: Acquired Immunodeficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV) has seriously threatened human health, so development of new, selective and safe non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) remains a high priority for medical research. Diaryltriazine (DATA) have been identified as a new class of potent nonnucleoside HIV-1 Reverse Transcriptase (RT) inhibitors. The study deals with Topomer CoMFA (Comparative Molecular Field Analysis) and molecular docking to explore the important features of DATA analogues for exerting potent HIV-1 RT inhibitors activity. Methods: In this work, 40 DATA analogues were studied using a combination of molecular modeling techniques including Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR), molecular docking, and Topomer CoMFA were used to build 3D-QSAR models. Results: The results show that the Topomer CoMFA analysis has the cross-validation q2 = 0.800, SDCV = 0.45, the non-cross-validated r2 = 0.958, SD = 0.21, and the correlation coefficient of external validation Q2 ext = 0.965 showed that the model is reasonable and credible, and has a good predictive ability. Then binding mode pattern of the compounds to the binding site of enzyme was confirmed and the mechanism of drug and acceptor was studied by docking studies, the results showed that the drug and GLU138, LYS101, THR139 sites have an obvious function, these researches have provided an useful information for designing more effective HIV-1IN inhibitors. Conclusion: A series of 40 DATAs analogues was subjected to a 3D-QSAR study. Using Topomer CoMFA 3D-QSAR method built model, and the model has shown a good predictive and statistical validation. Substituent with low electronic density in the R5 and R3 positions and substituent with high electronic density in the R2 and C2 positions will increase the biological activity, small substituent on R4 positions and naphthyloxy as the spacer group C6 substituent hydrophobic will increase biological activity. This effect is supported by Topomer CoMFA contour map and docking results of HIV-1RT inhibition active site, the results of the 3D-QSAR and docking analyses have provided a guide for the synthesis of new putative inhibitors for HIV-1RT to improved inhibitory activity.

3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation

Mesenchymal-epithelial transition factor (c-Met) is a member of receptor tyrosine kinase. It involves in various cellular signaling pathways which includes proliferation, motility, migration, and invasion. Over-expression of c-Met has been reported in various cancers. Hence, it is an ideal therapeutic target for cancer. The main objective of the study is to identify crucial residues involved in the inhibition of c-Met kinase and to design a series of potent imidazo [4,5-b] pyrazine derivatives as c-Met inhibitors. Docking was used to identify important active site residues involved in the inhibition of c-Met kinase which was further validated by 100 ns of molecular dynamics simulation and free energy calculation using molecular mechanics generalized born surface area. Furthermore, binding energy decomposition identified that residues Tyr1230, Met1211, Asp1222, Tyr1159, Met1160, Val1092, Ala1108, and Leu1157 contributed favorably to the binding stability of compound 32. Receptor-guided Comparative Molecular Field Analysis (CoMFA) (q2 = 0.751, NOC = 6, r2 = 0.933) and Comparative Molecular Similarity Indices Analysis (COMSIA) (q2 = 0.744, NOC = 6, r2 = 0.950) models were generated based on the docked conformation of the most active compound 32. The robustness of these models was tested using various validation techniques and found to be predictive. The results of CoMFA and CoMSIA contour maps exposed the regions favorable to enhance the activity. Based on this information, 27 novel c-Met inhibitors were designed. These designed compounds exhibited potent activity than the most active compound of the existing dataset.Communicated by Ramaswamy H. Sarma

Towards the In-silico Design of New HSP90 Inhibitors: Molecular Docking and 3D-QSAR CoMFA Studies of Tetrahydropyrido [4, 3-d] Pyrimidine Derivatives as HSP90 Inhibitors.

HSP90 is necessary for the conformational maturation of proteins, proteins disaggregation, folding newly synthesized peptides and the refolding of denatured proteins. The inhibition of HSP90 leads to proteasomal degradations of client proteins that finally kill cancer cells. In this research, molecular docking and comparative molecular field analysis (CoMFA) were used to investigate the interactions of tetrahydropyrido[4,3-d]pyrimidine derivatives with the N-terminal domain binding site of the HSP90 and predicting their inhibitory activities. A CoMFA model with five components and q2 of 0.81 was developed. R2 for training and test sets were 0.96 and 0.79, respectively. Based on extracted Contour maps for this CoMFA model, three new inhibitors with greater pIC50 with respect to the greatest active molecule in the data-set were designed by modifying molecule m45. Molecule m45 and designed inhibitors were docked to the N-terminal domain binding site of the HSP90. Designed inhibitors obtained lower binding energy with respect to m45. Based on extracted CoMFA contour maps, bulky substituents are favored for the R1 group and in R3 group, short and bulky substituents increase the activity of molecules. Less bulky and longer substituents are favored for R2. The molecular docking analysis of compound m45 with the N-terminal domain binding site of the HSP90 show hydroxyl group on phenyl ring is necessary to form hydrogen bonding with hydrophilic residues in binding site and a conserved water molecule. Molecule m45 has Pi-Sigma interaction with phenyl ring in the side chain of Phenylalanine 138 via isopropyl substituent on meta position of the phenyl ring. Also, Molecule m45 forms carbon-hydrogen bond with oxygen atoms at the side chain of Aspartic acid 54 and Asparagine 51 via its dimethylamine group. Others are Van der Waals interactions.

Toward the identification of a reliable 3D-QSAR model for the protein tyrosine phosphatase 1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) is an intracellular non-receptor phosphatase that is implicated in signal transduction of insulin and leptin pathways, thus PTP1B is considered as potential target for treating type II diabetes and obesity. The present article is an attempt to formulate the three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling of a series of compounds possessing PTP1B inhibitory activities using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The optimum template ligand-based models are statistically significant with great CoMFA (R2cv = 0.600, R2pred = 0.6760) and CoMSIA (R2cv = 0.624, R2pred = 0.8068) values. Molecular docking was employed to elucidate the inhibitory mechanisms of this series of compounds against PTP1B. In addition, the CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of PTP1B active site. The knowledge of structure-activity relationship and ligand-receptor interactions from 3D-QSAR model and molecular docking will be useful for better understanding the mechanism of ligand-receptor interaction and facilitating development of novel compounds as potent PTP1B inhibitors.

A ligand-based comparative molecular field analysis (CoMFA) and homology model based molecular docking studies on 3′, 4′-dihydroxyflavones as rat 5-lipoxygenase inhibitors: Design of new inhibitors

In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3′, 4′-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R2 values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q2 values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed.

3D-QSAR, molecular docking, and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.

Assessment of long-range transport potential of polychlorinated Naphthalenes based on three-dimensional QSAR models.

Experimentally determined octanol-air partition coefficients (K OA) for 43 polychlorinated naphthalene (PCN) congeners and experimentally determined subcooled liquid vapor pressures (P L) for 17 PCN congeners were used with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to generate three-dimensional quantitative structure-activity relationship (3D-QSAR) models. The data were used to predict K OA values for the other 32 congeners and P L values for the other 58 congeners. The CoMFA and CoMSIA model contour maps showed that the electrostatic fields of the PCN molecules are the most important factors affecting the K OA and P L values. The long-range transport potentials of several PCN homologs were assessed using the following grading system: high mobility (MoCNs), relatively high mobility (DiCNs to TeCNs), relatively low mobility (PeCNs to HeCNs) and low mobility (HeCNs and OCN). The PCN-2 molecule was modified using the contour maps of the two models, and the results showed that introducing an electronegative R1 substituent increased the K OA value but introducing an electropositive R6 substituent decreased the P L value. PCN-2 was in the high mobility class, but introducing these substituents moved the long-range transport potentials of the modified molecules to the relatively high mobility class.

An improvement on the prediction power of the 3D-QSAR CoMFA models using a hybrid of statistical and machine learning methods: a case study on γ‑secretase modulators of Alzheimer’s disease

A comparative molecular field analysis has been developed to study the three-dimensional quantitative structure–activity relationship of a series of triterpene-based γ-secretase modulators. We have performed the genetic algorithm on a large set of comparative molecular field analysis fields to select the most responsible fields contributing to inhibitory activities of these compounds against Alzheimer’s disease. The genetic algorithm-selected comparative molecular field analysis fields were introduced into the partial least squares and principal component analysis to reduce the dimensionality of the input features. The extracted partial least squares components were used as inputs to build partial least squares regression (genetic algorithm-partial least squares regression), and the extracted principal components were used as inputs for principal component regression (genetic algorithm-principal component regression) and support vector regression (genetic algorithm-principal component analysis-support vector regression). The classic three-dimensional quantitative structure–activity relationship comparative molecular field analysis analysis (partial least squares regression) is also carried out for the sake of comparison. The results show that among the constructed models, in terms of root mean squares and leave-one-out cross-validated R 2(q 2), the combination of principal component analysis and support vector machine can effectively improve the prediction performance (RMSEtrain = 0.231, RMSEtest = 0.360, and q 2 = 0.638) compared with PLSR (RMSEtrain = 0.415, RMSEtest = 0.680, and q 2 = 0.311). The performances of the genetic algorithm-principal component regression and genetic algorithm-partial least squares regression were also comparable but less powerful than genetic algorithm-principal component analysis-support vector regression. Finally, based on the information derived from the comparative molecular field analysis contour map, some key features for increasing the activity of γ-secretase modulators have been identified to design new triterpene-based Alzheimer’s disease drugs.

Theoretical analysis of somatostatin receptor 5 with antagonists and agonists for the treatment of neuroendocrine tumors.

We report on SSTR5 receptor modeling and its interaction with reported antagonist and agonist molecules. Modeling of the SSTR5 receptor was carried out using multiple templates with the aim of improving the precision of the generated models. The selective SSTR5 antagonists, agonists and native somatostatin SRIF-14 were employed to propose the binding site of SSTR5 and to identify the critical residues involved in the interaction of the receptor with other molecules. Residues Q2.63, D3.32, Q3.36, C186, Y7.34 and Y7.42 were found to be highly significant for their strong interaction with the receptor. SSTR5 antagonists were utilized to perform a 3D quantitative structure-activity relationship study. A comparative molecular field analysis (CoMFA) was conducted using two different alignment schemes, namely the ligand-based and receptor-based alignment methods. The best statistical results were obtained for ligand-based ([Formula: see text], [Formula: see text] = 0.988, noc = 4) and receptor-guided methods (docked mode 1:[Formula: see text], [Formula: see text], noc = 5), (docked mode 2:[Formula: see text] = 0.555, [Formula: see text], noc = 5). Based on CoMFA contour maps, an electropositive substitution at [Formula: see text], [Formula: see text] and [Formula: see text] position and bulky group at [Formula: see text] position are important in enhancing molecular activity.

3D-QSAR analysis of a series of S-DABO derivatives as anti-HIV agents by CoMFA and CoMSIA

In this study, we retrieved a series of 59 dihydroalkylthio-benzyloxopyrimidine (S-DABO) derivatives, which is a class of highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) reported from published articles, and analysed them with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Statistically significant three-dimensional quantitative structure–activity relationship (3D-QSAR) models by CoMFA and CoMSIA were derived from a training set of 46 compounds on the basis of the rigid body alignment. Further, the predictive ability of the QSAR models was validated by a test set of 13 compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some steric and electrostatic features for improving the activities of these inhibitors, and we validated the 3D-QSAR results by a molecular docking method. On the basis of the obtained results, we designed a new series of S-DABO derivatives with high activities. Therefore, this study could be utilized to design more potent S-DABO analogues as anti-HIV agents.

Molecular docking and QSAR analysis of naphthyridone derivatives as ATAD2 bromodomain inhibitors: application of CoMFA, LS-SVM, and RBF neural network

In this research, molecular docking and QSAR models based on comparative molecular field analysis-like, least squares-support vector machine, and radial basis function neural network were used to investigate the interactions of naphthyridone derivatives with the binding site of ATAD2 and predict their activities. First molecular docking was used to investigate binding interactions between molecules with the greatest, the lowest and with medium activities and the binding site of ATAD2, then comparative molecular field analysis was used to model and predict their activities. Squared correlation coefficient (R 2) for training and test sets of comparative molecular field analysis-like model and its leave-one-out cross validation (Q 2) were 0.87, 0.83, and 0.78, respectively. The contributions of steric and electrostatic fields in the building of model were 49.64 and 50.36 %, respectively. Comparative molecular field analysis contour maps were extracted and interpreted to help the design of new molecules with greater activity. Principal component analysis was performed on comparative molecular field analysis descriptors and extracted scores were used as input variable to develop more reliable least squares-support vector machine and radial basis function neural network models. R 2 values for training and test sets of least squares-support vector machine were 0.82 and 0.84, respectively, and Q 2 parameter for its training set was 0.82. These results indicate least squares-support vector machine has slightly higher predictive power compared to the comparative molecular field analysis model. R 2 values for training and test sets of radial basis function neural network model were 0.89 and 0.90, respectively, and its squared correlation coefficient for leave-one-out cross validation was 0.87 that shows radial basis function neural network model has the greatest predictive power. All models have been validated with several statistical parameters and their applicability domains show all models were reliable.

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model.

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q2) value of 0.597 and correlation coefficients (r2) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q2 and r2 of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r2pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.

Pharmacophore interactions analysis and prediction of inhibitory activity of 1,7-diazacarbazoles as checkpoint kinase 1 inhibitors: application of molecular docking, 3D-QSAR and RBF neural network

In the present study, we mainly focused on new synthesized 1,7-diazacarbazole derivatives (44 active molecules) as Chk1 inhibitors to build 3D-QSAR model. Comparative molecular field analysis (CoMFA) model with three principal components was developed. The relative contributions in building of CoMFA model were 64.41 % for steric field and 35.59 % for electrostatic field. R 2 values for training and test sets of CoMFA model were 0.8724 and 0.7818, respectively, and squared correlation coefficient for leave-one-out cross-validation test (q 2) was 0.6753. To improve the predictive power, a new 3D-QSAR model was developed by using radial basis function network (RBFN) and score of CoMFA interactions energy values as input variables. Scores 1, 2 and 3 were used as input variables, and a RBFN model with seven centers and spread value equal to 95 was developed to create a nonlinear 3D-QSAR model. R 2 values for training and test sets were 0.9613 and 0.8564, and q 2 for leave-one-out cross-validation test was 0.9258. Docking of all molecules to 3DX ligand binding site of Chk1 receptor indicated six interactions as pharmacological interactions between compounds and binding site of receptors. These pharmacological interactions were hydrogen bonding with LEU-15 and GLU-85 in main chain and four van der Waals interactions with LEU-15, VAL-23, TYR-86 and LEU-137 in side chain. CoMFA contour plots were used to design new inhibitors, and inhibitory activity of each compound was predicted by using CoMFA and RBFN models.

Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models

Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q2) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825–0.969 (>0.5), the correlation coefficient (r2) obtained was in the range of 0.957–1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070–0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3′-, 5- and 6- positions, which could reduce the PCB migration ability. These results are expected to be beneficial in predicting logKOA values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the global migration behaviour of PCBs.

3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods

Dipeptidyl peptidase-4 (DPP-4) is one of the most attractive targets in the area of type 2 diabetes treatment. Till date, many structurally diverse DPP-4 inhibitors have been explored and published. To identify essential structural features of these diverse DPP-4 inhibitors responsible for antidiabetic activity, three-dimensional quantitative structure–activity relationship analyses were carried out on 36 reported quinoline and isoquinoline derivatives. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) using three different alignment methods. The distill rigid body alignment-based CoMFA and CoMSIA models gave best significant results for 27 training set molecules, with cross-validated coefficients (q 2) of 0.803 and 0.826, respectively, and conventional coefficients (r 2) of 0.991 and 0.983, respectively. Validation by test set of nine molecules gave excellent predicted correlation coefficients (r pred 2 ) of 0.874 and 0.847 for CoMFA and CoMSIA models, respectively. Detailed analysis of CoMFA and CoMSIA contour maps revealed many helpful structural insights to improve the activity of newly designed quinoline and isoquinoline derivatives as DPP-4 inhibitors for the treatment of type-2 diabetes.

Synthesis, crystal structure, herbicidal activities and 3D-QSAR study of some novel 1,2,4-triazolo[4,3-a]pyridine derivatives.

1,2,4-Triazolo[4,3-a]pyridine derivatives represent a new series of compounds that possess good herbicidal activity against Echinochloa crusgalli (L.) Beauv., Setaria faberii, Digitaria sanguinalis (L.) Scop., Brassica juncea Coss., Amaranthus retroflexus L. and Eclipta prostrata L. A total of 23 novel 1,2,4-triazolo[4,3-a]pyridine derivatives were synthesised and identified by (1) H NMR, IR, single-crystal X-ray diffraction, mass-spectroscopic and elemental analysis, and their herbicidal activities were tested against E. crusgalli (L.) Beauv., S. faberii, D. sanguinalis (L.) Scop., B. juncea Coss., A. retroflexus L. and E. prostrata L. at 150 g a.i. ha(-1) . It was found that the title compound 8-chloro-3-(4-propylphenyl)-[1,2,4]-triazolo[4,3-a]pyridine possesses high herbicidal activity and a broad spectrum against the 22 test weeds, with an inhibition effect of about 50% at a dosage of 37.5 g a.i. ha(-1) , and is safe for corn, cotton and rice at a dosage of 150 g a.i. ha(-1) . Furthermore, comparative molecular field analysis contour models were established to study the structure-activity relationship of the title compounds. It is possible that, with further structure modification, 1,2,4-triazolo[4,3-a]pyridine derivatives, which possess good herbicidal activities, may become novel lead compounds for the development of herbicides against dicotyledonous weeds.