A CoMFA study of COX-2 inhibitors with receptor based alignment

A diverse set of 53 cyclooxygenase-2 (COX-2) inhibitors which were aligned in two different ways were subjected to CoMFA analysis. The first method of alignment of the molecules was based on the binding information sourced from the crystallographic study, from which CoMFA Model 1 was derived. The second mode of alignment was generated by docking the inhibitors in the binding pocket using the DOCK and AFFINITY suite of programs; this gave a second model. The CoMFA Model 2 was slightly better than Model 1 in terms of the statistical parameters r(2) and q(2). The two models could predict very well the activity of a test set of diverse molecules, with a predictive r(2) of 0.593 and 0.768, respectively. Besides the QSAR results, the docking studies give a deep insight into the H-bonding interactions between the inhibitors and residues in the active site of the enzyme, which can be exploited in designing better inhibitors. Useful ideas on activity improvement could be gleaned from these models.     

3D-QSAR and molecular docking studies of 1,3,5-triazene-2,4-diamine derivatives against r-RNA: novel bacterial translation inhibitors

Aminoglycoside mimetics inhibit bacterial translation by interfering with the ribosomal decoding site. To elucidate the structural properties of these compounds important for antibacterial activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to a set of 56 aminoglycosides mimetics. The successful CoMFA model yielded the leave-one-out (LOO) cross-validated correlation coefficient (q(2)) of 0.708 and a non-cross-validated correlation coefficient (r(2)) of 0.967. CoMSIA model gave q(2)=0.556 and r(2)=0.935. The CoMFA and CoMSIA models were validated with 36 test set compounds and showed a good r(pred)(2) of 0.624 and 0.640, respectively. Contour maps of the two QSAR approaches show that electronic effects dominantly determine the binding affinities. These obtained results were agreed well with the experimental observations and docking studies. The results not only lead to a better understanding of structural requirements of bacterial translation inhibitors but also can help in the design of novel bacterial translation inhibitors.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses of choline acetyltransferase inhibitors

As a basis for predicting structural features that may lead to the design of more potent and selective inhibitors of choline acetyltransferase (ChAT), the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on a series of trans-1-methyl-4-(1-naphthylvinyl)pyridinium (MNVP+) analogs, which are known ChAT inhibitors. 3D-QSAR studies were carried out using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Since these inhibitors have extremely shallow potential energy minimum energy wells and low barriers to rotation, two dihedral angles unique to these inhibitors were systematically modified to reflect the energetically preferred conformations as determined by force field calculations. An optimum alignment rule was devised based on the conformations obtained from the molecular mechanics studies, using a common substructure alignment method. The studies involve a set of 21 compounds and experimentally determined molar IC50 values were used as the dependent variable in the analysis. The 3D-QSAR models have conventional r2-values of 0.953 and 0.954 for CoMFA and CoMSIA, respectively; similarly, cross-validated coefficient q2-values of 0.755 and 0.834 for CoMFA and CoMSIA, respectively, were obtained. On the basis of these predictive r2-values the model was tested using previously determined IC50 values. CoMSIA 3D-QSAR yielded better results than CoMFA.     

Insight into the structural requirements of proton pump inhibitors based on CoMFA and CoMSIA studies

In the present study, a series of 179 quinoline and quinazoline heterocyclic analogues exhibiting inhibitory activity against Gastric (H+/K+)-ATPase were investigated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods. Both the models exhibited good correlation between the calculated 3D-QSAR fields and the observed biological activity for the respective training set compounds. The most optimal CoMFA and CoMSIA models yielded significant leave-one-out cross-validation coefficient, q(2) of 0.777, 0.744 and conventional cross-validation coefficient, r(2) of 0.927, 0.914 respectively. The predictive ability of generated models was tested on a set of 52 compounds having broad range of activity. CoMFA and CoMSIA yielded predicted activities for test set compounds with r(pred)(2) of 0.893 and 0.917 respectively. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r(pred)(2) based on the mean activity of test set compounds can accurately estimate external predictivity. The factors affecting activity were analyzed carefully according to standard coefficient contour maps of steric, electrostatic, hydrophobic, acceptor and donor fields derived from the CoMFA and CoMSIA. These contour plots identified several key features which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel peptic-ulcer inhibitors prior to their synthesis.     

Structural investigation of PAP derivatives by CoMFA and CoMSIA reveals novel insight towards inhibition of Bcr-Abl oncoprotein

Molecular modeling by 3D-QSAR comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed on a series of phenylaminopyrimidine-based (PAP) Bcr-Abl inhibitors. The chemical structures of 63 PAP analogues were aligned using a template extracted from the crystal structure of STI571 bound to Abl kinase. Subsequently, the structures built were divided into training and test sets that include 53 and 10 compounds, respectively. Statistical results showed that the 3D-QSAR models generated from CoMSIA were superior to CoMFA (CoMSIA; q2=0.66, r2=0.94, N=3, F=139.09, r2pred=0.64 while CoMFA; q2=0.53, r2=0.73, N=3, F=43.53, r2pred=0.61). Based on the contour interpretation, the attachment of hydrophobic and bulky groups to the phenyl and pyrrolidine (D- and E-ring of NS-187, respectively) along with highly electronegative groups around the D-ring are important structural features for the design of second-generation Bcr-Abl inhibitors. The generated models are predictive based on reproducible values of the predicted compared with experimental activities in the test set. Further, the complementary analysis of contour maps to the Bcr-Abl binding site suggested the anchor points for binding affinity.     

3D QSAR studies on a series of potent and high selective inhibitors for three kinases of RTK family

For targets belonging to the same family of receptors, inhibitors often act at more than one biological target and produce a synergistic effect. Separate CoMFA and CoMSIA models were developed from our data set for the KDR, cKit and Tie-2 inhibitors. These models showed excellent internal predictability and consistency, and validation using test-set compounds yielded a good predictive power for the pIC(50) value. The field contour maps (CoMFA and CoMSIA) corresponding to the KDR, cKit and Tie-2 kinase subtypes reflected the characteristic similarities and differences between these types. These maps provided valuable information to facilitate structural modifications of the inhibitor to increase selectivity for the KDR over cKit and Tie-2.     

Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

The estrogen receptor (ER) is an important drug target for the development of novel therapeutic agents for the treatment of breast cancer. Progress towards the design of more potent and selective ER modulators requires the optimization of multiple ligand-receptor interactions. Comparative molecular field analyses (CoMFA) and hologram quantitative structure-activity relationships (HQSAR) were conducted on a large set of ERalpha modulators. Two training sets containing either 127 or 69 compounds were used to generate QSAR models for in vitro binding affinity and potency, respectively. Significant correlation coefficients (affinity models, CoMFA, r(2)=0.93 and q(2)=0.79; HQSAR, r(2)=0.92 and q(2)=0.71; potency models, CoMFA, r(2)=0.94 and q(2)=0.72; HQSAR, r(2)=0.92 and q(2)=0.74) were obtained, indicating the potential of the models for untested compounds. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel ERalpha modulators having improved affinity and potency.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

An investigation of structurally diverse carbamates for acetylcholinesterase (AChE) inhibition using 3D-QSAR analysis

In order to identify the essential structural features and physicochemical properties for acetylcholinesterase (AChE) inhibitory activity in some carbamate derivatives, the systematic QSAR (Quantitative Structure Activity Relationship) studies (CoMFA, advance CoMFA and CoMSIA) have been carried out on a series of (total 78 molecules) taking 52 and 26 molecules in training and test set, respectively. Statistically significant 3D-QSAR (three-dimensional Quantitative Structure Activity Relationship) models were developed on training set molecules using CoMFA and CoMSIA and validated against test set compounds. The highly predictive models (CoMFA q(2)=0.733, r(2)=0.967, predictive r(2)=0.732, CoMSIA q(2)=0.641, r(2)=0.936, predictive r(2)=0.812) well explained the variance in binding affinities both for the training and the test set compounds. The generated models suggest that steric, electrostatic and hydrophobic interactions play an important role in describing the variation in binding affinity. In particular the carbamoyl nitrogen should be more electropositive; substitutions on this nitrogen should have high steric bulk and hydrophobicity while the amino nitrogen should be electronegative in order to have better activity. These studies may provide important insights into structural variations leading to the development of novel AChE inhibitors which may be useful in the development of novel molecules for the treatment of Alzheimer's disease.     

3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3beta inhibitors

Glycogen synthase kinase-3 (GSK-3), a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders and Alzheimer's disease. It is important to inhibit GSK-3 selectively and the net effect of the GSK-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure activity relationship (3D-QSAR) studies on novel class of pyrazolopyrimidine derivatives as GSK-3 inhibitors reported to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for alignment of the molecules. Statistically significant CoMFA and CoMSIA models were generated using 49 molecules in training set. By applying leave-one-out (LOO) cross-validation study, r(cv)2 values of 0.53 and 0.48 for CoMFA and CoMSIA, respectively and non-cross-validated (r(ncv)2) values of 0.98 and 0.92 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of CoMFA and CoMSIA models was determined using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)2) of 0.47 and 0.48, respectively, indicating good predictive power. Based upon the information derived from CoMFA and CoMSIA contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new pyrazolopyrimidine derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems; hence suggesting that newly designed molecules can be more potent and selective towards GSK-3beta inhibition.     

Generation and validation of the first predictive pharmacophore model for cyclin-dependent kinase 9 inhibitors

A three-dimensional (3D) pharmacophore modelling approach was applied to a diverse data set of known cyclin-dependent kinase 9 (CDK9) inhibitors. Diversity sampling and principal components analysis (PCA) were employed to ensure the rational selection of representative training sets. Twelve statistically robust pharmacophore models were generated using the HypoGen algorithm. The resulting models showed high homology and indicated great convergence in ascertaining pharmacophoric features essential for CDK9 inhibitory activity. One of the best models (Hypo 6) was assessed further by external predictive capability, randomization test, as well as its performance in virtual screening. The capability of the resulting models to reliably predict the inhibitory activity of external data sets and discriminate active structures from general databases would assist the identification and optimization of novel CDK9 inhibitors.     

新非核苷喹诺酮类HIV-1逆转录酶抑制剂的CoMFA研究

目的：应用比较分子力场法(COMFA)研究一系列喹诺酮类对HIV-1逆转录酶抑制活性的三维定量构效关系,为进一步抗HIV药物设计提供理论依据。方法和结果：在研究的29个化合物中,用比较分子力场法得到一个CoMFA模型,交叉验证系数为q~2=0.556,具有较高的预测能力及合理性,非交叉验证模型相关系数分别为r~2=0.998,标准偏差SE=0.044,F= 401.038；结论：此模型对设计和预测高活性的喹诺酮类HIV-1逆转录酶抑制活性的化合物有一定可靠性。     

CoMFA based de novo design of pyridazine analogs as PTP1B inhibitors

PTP1B plays an important role as a negative regulator in insulin and leptin signaling pathways. Potent and orally active PTP1B inhibitors can act as potential agents for the treatment of Type 2 diabetes and obesity. CoMFA (Comparative Molecular Field Analysis) and de novo ligand design using LeapFrog (LF) studies were performed on pyridazine analogs, reported to be selective and non-competitive inhibitors of PTP1B. A robust model was developed which produced statistically significant results with cross-validated and conventional correlation coefficients of 0.619 and 0.990, respectively. Further, the robustness of the model was verified by bootstrapping analysis. LeapFrog (LF) program is a de novo drug discovery tool, which uses CoMFA maps to generate hypothetical cavity and ligands. As the crystal structure of PTP1B-pyridazine complex is not yet known, the contours of CoMFA model was used to serve as a pharmacophoric model to generate hypothetical cavity for LeapFrog calculations. Ligands were optimized using this concept.     

N,N-二甲基-2-溴苯乙胺类衍生物活性与CoMFA、CoMSIA的研究

用比较分子力场分析(CoMFA)法和比较分子相似性指数分析(CoMSIA)法,建立N,N-二甲基-2-溴苯乙胺类化合物的3D-QSAR模型。CoMFA模型中,其交叉验证系数q2=0．792,传统的相关系数R2=0．955(R=0．978),相应立体场贡献为77．4％、静电场贡献为22．6％,优于文献的报导。CoMSIA研究中,其交叉验证系数q2=0．757,传统的相关系数R2=0．917 (R=0．958),其疏水场、立体场、静电场贡献依次为:42．9％、39．5％、17．6％。用两种模型分别预测检测集分子的活性,结果与实验值较吻合。说明所建的模型具有较好的预测能力。通过分析CoMFA分子场等值线图,可为优化N,N-二甲基-2-溴苯乙胺类衍生物的结构提供理论指导。