Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug design and discovery. To develop novel potent neuraminidase inhibitors (NAI), Surflex-Dock was employed to dock 40 hydrophobic p-aminosalicylic acid derivatives into the active site of NA. The 3D-quantitative structure-activity relationship studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 40 molecules. Both CoMFA (q(2) = 0.628, r(2) = 0.697) and CoMSIA (q(2) = 0.746, r(2) = 0.816) gave reasonable results. A preliminary pharmacokinetic profile of these NAI was also performed on the basis of Volsurf predictions. The results obtained from this study will be useful in the design of novel potent NAI.     

Pharmacophore modeling, 3D-QSAR studies, and in-silico ADME prediction of pyrrolidine derivatives as neuraminidase inhibitors

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug development. To develop potent NA inhibitors, pharmacophore models were generated by genetic algorithm with linear assignment for hypermolecular alignment of data sets. 3D-QSAR studies were carried out on 49 molecules. Both comparative molecular field analysis (q(2) = 0.720 and r(2) = 0.947) and comparative molecular similarity indices analysis (q(2) = 0.644 and r(2) = 0.885) yielded reasonable results. A preliminary pharmacokinetic profile of these neuraminidase inhibitors was predicted using Volsurf module.     

Bioassay of ferulic acid derivatives as influenza neuraminidase inhibitors

Four series of ferulic acid derivatives were designed, synthesized, and evaluated for their neuraminidase (NA) inhibitory activities against influenza virus H1N1 in vitro. The pharmacological results showed that the majority of the target compounds exhibited moderate influenza NA inhibitory activity, which was also better than that of ferulic acid. The two most potent compounds were 1m and 4a with IC₅₀ values of 12.77 ± 0.47 and 12.96 ± 1.34 μg/ml, respectively. On the basis of the biological results, a preliminary structure–activity relationship (SAR) was derived and discussed. Besides, molecular docking was performed to study the possible interactions of compounds 1p , 2d , 3b , and 4a with the active site of NA. It was found that the 4-OH-3-OMe group and the amide group (CON) of ferulic acid amide derivatives were two key pharmacophores for NA inhibitory activity. It is meaningful to further modify the natural product ferulic acid to improve its influenza NA inhibitory activity.     

Binding site analysis, 3D-QSAR studies, and molecular design of flavonoids derivatives as potent neuraminidase inhibitors

Binding site analysis of flavonoids derivatives indicated that Arg152, Trp178, Ile222, Glu227, and Ala246 were the key residues in the active pocket of 1nnc. Main influencing factors of interactions between flavonoids derivatives and neuraminidase (NA) were hydrogen bond and electrostatic. Meanwhile, 3D-QSAR models of flavonoids derivatives were constructed to understand chemical–biological interactions governing their activities toward NA. The developed 3D-QSAR models were robust and had good predictive capabilities. R ², Q ², R _test ² , and Q _ext ² of the CoMFA and CoMSIA models were 0.816 and 0.929, 0.607 and 0.750, 0.507 and 0.642, and 0.478 and 0.568, respectively. Moreover, hydrogen bonds and electrostatic factors highly contributed to inhibitory activity, which were unanimous in the docking results. In addition, based on the most active sample ID33, seven new compounds with high inhibitory activity and docking score were obtained.     

Docking and 3D-QSAR investigations of pyrrolidine derivatives as potent neuraminidase inhibitors

Docking studies of pyrrolidine derivatives indicated that Trp178, Arg371, and Tyr406 were the key residues in the active pocket of influenza neuraminidase (NA). Hydrogen bond and electrostatic factors mainly influenced interactions between pyrrolidine derivatives and NA. Moreover, there was a significant correlation between binding affinity (total scores) and the experimental pIC(50) . Meanwhile, 3D-QSAR models of 87 pyrrolidine derivatives were developed to understand chemical-biological interactions governing their activities toward NA. Furthermore, R(2) , Q(2) , , and of the models were from 0.731 to 0.830, from 0.560 to 0.611, from 0.762 to 0.875, and from 0.649 to 0.856, respectively. QSAR modeling results elucidated that hydrogen bonds and electrostatic factors highly contributed to inhibitory activity, which was unanimous in the docking results.     

3D-QSAR CoMFA/CoMSIA studies on 5-aryl-2,2-dialkyl-4-phenyl-3(2H)-furanone derivatives, as selective COX-2 inhibitors

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were performed on a series of 5-aryl-2,2-dialkyl-4-phenyl-3(2H)-furanone derivatives, as selective cyclooxygenase-2 (COX-2) inhibitors. Ligand molecular superimposition on the template structure was performed by the atom/shape based root mean square fit and database alignment methods. Removal of three outliers from the initial training set of 49 molecules improved the predictivity of the model. The statistically significant model was established of 36 molecules, which were validated by a test set of ten compounds. The atom and shape based root mean square alignment (IV) yielded the best predictive CoMFA model [R2(cv) = 0.664, R2 (non-cross-validated square of correlation coefficient) = 0.916, F value = 47.341, R2(bs) = 0.947 with six components, standard error of prediction36 = 0.360 and standard error of estimate36 = 0.180] while the CoMSIA model yielded [R2(cv) = 0.777, R2 (non-cross-validated square of correlation coefficient) = 0.905, F value = 66.322, R2(bs) = 0.933 with four components, standard error of prediction36 = 0.282 and standard error of estimate36 = 0.185]. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that steric, electrostatic, hydrophobic (lipophilic) and hydrogen bond donor substituents play a significant role in COX-2 inhibitory activity and selectivity of the compounds. The data generated from the present study will further help design novel, potent and selective COX-2 inhibitors.     

3-羧基香豆素类乳酸转运抑制剂的三维定量构效关系研究

目的 建立3-羧基香豆素类乳酸转运抑制剂三维定量构效关系(3D-QSAR)模型.方法 采用分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)来研究3-羧基香豆素类乳酸转运抑制剂的构效关系.结果 建立了合理、可靠的3-羧基香豆素类乳酸转运抑制剂CoMFA(q2=0.630,r2 =0.994,rpred2=0.909)和CoMSIA(q2=0.676,r2=0.972,rpred2 =0.574)模型.结论 构建的3D-QSAR模型揭示了3-羧基香豆素类化合物的结构和生物活性间的关系,可为该类乳酸转运抑制剂的进一步优化设计提供科学依据.     

Aromatic sialic acid analogues as potential inhibitors of influenza virus neuraminidase.

The influenza virus neuraminidase (NA) is an enzyme essential for viral infection and offers a potential target for antiviral drug development. We aimed our research at the synthesis of non-carbohydrate molecules able to inhibit NA as transition-state analogues. Aromatic sialic acid analogues (compound 5 and compound 10) were synthesised in good yields starting from commercially available benzoic acids using a suitable synthetic strategy.     

Docking study on chlorogenic acid as a potential H5N1 influenza A virus neuraminidase inhibitor

Docking simulation between chlorogenic acid and H5N1 influenza virus neuraminidase (NA) was performed and the binding free energies of the best pose and average for the best three different poses of H5N1 NA–chlorogenic acid complex are −9.71 and −9.27 kcal/mol, respectively, which is lower than those of H5N1 NA–oseltamivir complex (−7.13 and −6.39 kcal/mol) by using ArgusLab docking method. The hydrogen bonds could be formed between chlorogenic acid and the H5N1 NA amino acid residues Arg156 and Thr439. Arg152 from the 150-cavity makes polar contact with the –COOH group in chlorogenic acid. Chlorogenic acid could be a potential H5N1 influenza A virus NA inhibitor.     

噻唑类衍生物二氢乳清酸脱氢酶抑制剂的三维定量构效关系研究

目的 应用三维定量构效关系（3D-QSAR）研究噻唑类衍生物结构的二氢乳清酸脱氢酶抑制活性,为该类药物的设计和筛选提供可靠的理论依据。方法 针对38个以噻唑为基本骨架的二氢乳清酸脱氢酶抑制剂,分别应用分子力场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）2种经典的方法进行了三维定量构效关系（3D-QSAR）研究,建立相关模型,验证模型的预测能力,三维等势图分析噻唑类衍生物结构与活性的关系。结果 CoMFA模型的交叉验证系数q²为0.796,相关系数r²为0.978;CoMSIA模型的q²以及r²分别为0.721和0.976;2种模型对化合物的活性预测与实际值接近;三维等势图可以全面直观的分析化合物结构对其活性的影响。结论 该3D-QSAR模型三维等势图揭示了结构特征与抑制活性的关系,模型具有较好的预测能力和较强的稳定性,为进一步开发研究打下了较好的基础。     

3D-QSAR and docking studies of piperidine carboxamide derivatives as ALK inhibitors

Anaplastic lymphoma kinase (ALK) is an important and attractive target for the design of new anticancer drugs. In the present study, quantitative structure–activity relationship (QSAR) models of piperidine carboxamide derivatives against ALK were developed by CoMFA and CoMSIA approaches. Both the CoMFA and CoMSIA models yielded significant statistical results. The results of the QSAR model indicated the importance of steric, electrostatic, and hydrophobic properties in the potent ALK inhibitors. Furthermore, molecular docking of the most active compound 25 with the active site of ALK was also investigated. The outcomes of this study may result in a better understanding of the inhibition mechanism of ALK and aided in the development of new and more potent anticancer drugs.     

Molecular modelling studies on flavonoid derivatives as dual site inhibitors of human acetyl cholinesterase using 3D-QSAR,pharmacophore and high throughput screening approaches

Alzheimer’s disease (AD) is a multi-factorial neurodegenerative disease that affects millions of elderly people worldwide. Due to its massive occurrence and severity, there is continuing and compelling need for the development of novel and effective drugs for improved treatment of AD. Since AD is characterized by the deficiency in cholinergic neurotransmission, acetyl cholinesterase (AChE) has been considered as a promising drug target. Herein we triggered our effort to design novel and potential inhibitors of AChE using a set of 24 flavonoid compounds having inhibitory activity against AChE. We carried out 3D-QSAR-based and pharmacophore-based identification of novel natural lead candidates. The 3D-QSAR model obtained using partial least square regression showed satisfactory parametric values (r ² = 0.8227, q ² = 0.6833 and pred-r ² = 0.7893). Amongst total 14 pharmacophore hypothesis generated the one possessing following five features: one hydrogen bond acceptor, two hydrophobic regions and two aromatic rings, was considered to be the best pharmacophore hypothesis. Above-described robust and validated 3D-QSAR and pharmacophore models were used for carrying out prospective generic prediction and virtual screening on large natural compound libraries. The screened molecules from both the approaches were subjected for further docking analysis to reveal the binding modes of actions of these ligands. All the ligands were found to bind with both catalytic and anionic subsite of AChE. The molecular insights obtained from this study will be of high value for design and development of novel drugs for AD, possessing improved binding properties and low toxicity to human.     

Molecular docking and 3D-QSAR studies of 2-substituted 1-indanone derivatives as acetylcholinesterase inhibitors

Aim： To explore the binding mode of 2-substituted 1-indanone derivatives with acetylcholinesterase （ACHE） and provide hints for the future design of new de- rivatives with higher potency and specificity. Methods： The GOLD-docking con- formations of the compounds in the active site of the enzyme were used in subse- quent studies. The highly reliable and predictive three-dimensional quantitative structure-activity relationship （3D-QSAR） models were achieved by comparative molecular field analysis （CoMFA） and comparative molecular similarity analysis （CoMSIA） methods. The predictive capabilities of the models were validated by an external test set. Moreover, the stabilities of the 3D-QSAR models were veri- fied by the leave-4-out cross-validation method. Results： The CoMFA and CoMSIA models were constructed successfully with a good cross-validated coef- ficient （q2） and a non-cross-validated coefficient （r2）. The q2 and r2 obtained from the leave- 1-out cross validation method were 0.784 and 0.974 in the CoMFA model and 0.736 and 0.947 in the CoMSIA model, respectively. The coefficient isocontour maps obtained from these models were compatible with the geometrical and physi- cochemical properties of ACHE. Conclusion： The contour map demonstrated that the binding affinity could be enhanced when the small protonated nitrogen moi- ety was replaced by a more hydrophobic and bulky group with a highly partial positive charge. The present study provides a better understanding of the inter- action between the inhibitors and ACHE, which is helpful for the discovery of new compounds with more potency and selective activity.     

QSAR studies for some thiazolidine-2,4-dione derivatives as PIM-2 kinase inhibitors

Quantitative structure–activity relationship (QSAR) studies were performed on a series of 21 thiazolidine-2,4-dione derivatives to find the structural requirements for PIM-2 kinase inhibitory activity by two-dimensional (2D-QSAR), group-based (G-QSAR) and three-dimensional (3D-QSAR) studies. In the present study, widely used technique viz. stepwise forward–backward (SW-FB) has been applied for the development of 2D- and G-QSAR as variable selection method. The statistically significant best 2D-QSAR model was developed by partial least squares regression (PLSR) having r ² = 0.78, q ² = 0.63 with pred_r ² = 0.78. The statistically significant best G-QSAR model was developed by PLSR method having r ² = 0.89, q ² = 0.79 and pred_r ² = 0.82. The 3D-QSAR studies were performed by k-nearest neighbor molecular field analysis along with genetic algorithm method which showed q ² = 0.64 and pred_r ² = 0.94. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (PHE 43, ASP 124, ASP 182 and GLU 83) of PIM-2 enzyme (PDB ID: 3IWI). The results of this study may be useful to (medicinal) chemists to design more potent thiazolidine-2,4-dione analogs as PIM-2 kinase inhibitors.     

QSAR,in silico docking and in vitro evaluation of chalcone derivatives as potential inhibitors for H1N1 virus neuraminidase

Thirty three chalcones were synthesized and tested on viral H1N1 neuraminidase activity by using MUNANA assay [2′-(4-methylumbelliferyl)-α-d-N-acetylneuraminic acid] assay with DANA (2,3-didehydro-2-deoxy-N-acetylneuraminic acid) was used as standard. 2D and 3D-quantitative structure?activity relationship models have been successfully developed with a good correlative and predictive ability for quantitative structure?activity relationships of these chalcone derivatives. Result from the 2D-quantitative structure?activity relationship model indicates that electrostatic parameter enhanced bioactivity of the chalcones while steric substituents diminished their potency as H1N1 neuraminidase inhibitors. 3D-quantitative structure?activity relationship model showed the importance of the position of the hydroxyl group in chalcone derivatives which can influence on hydrophobicity, hydrogen bond donor and aromatic ring features that enhance the biological activity. Finally, docking studies showed that chalcones MC8 and MC16 with low C docker interaction energies and higher numbers of hydrogen bonding have better inhibitory activity against viral H1N1 neuraminidase.