3D-QSAR studies on thieno[3,2-d]pyrimidines as phosphodiesterase IV inhibitors

Cyclic nucleotide phosphodiesterase IV (PDE IV) inhibitors find utility in asthma and Chronic Obstructive Pulmonary Disease (COPD) therapy. A series of 29 thieno[3,2-d]pyrimidines with affinity for PDE IV was subjected to three dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Both CoMFA and CoMSIA provided statistically valid models with good correlative and predictive power. The incorporation of hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields showed insignificant improvement in CoMSIA model. The 3D-QSAR models provide information for predicting the affinity of related compounds and designing more potent inhibitors.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

3D-QSAR of N-myristoyltransferase inhibiting antifungal agents by CoMFA and CoMSIA methods

A series of benzofuran antifungals was examined to determine the structural requirements of N-myristoyltransferase (Nmt) enzyme inhibition by three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Evaluation of 20 compounds (training set) served to establish the model, which was validated by evaluation of a set of 6 compounds (test set). The lowest energy conformer of the most active molecule obtained from systematic search was used as the template structure for the alignment. The best predictions were obtained with the CoMFA model from RMS fit, with r(2)(cv)=0.828, r(2)(conv)=0.989, r(2)(pred)=0.754 and with the CoMSIA model combining hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields with r(2)(cv)=0.821, r(2)(conv)=0.978 and r(2)(pred)=0.747. The models obtained from the present study can be useful for the development of new Nmt inhibitors as potential antifungals. The docking studies were also carried out wherein the active and inactive molecules were docked into the active site of the recently reported Candida albicans Nmt (CaNmt) crystal structure to analyze enzyme-inhibitor interactions. The results obtained from the present 3D-QSAR and docking studies were found complimentary.     

Pharmacophore modeling, virtual screening and 3D-QSAR studies of 5-tetrahydroquinolinylidine aminoguanidine derivatives as sodium hydrogen exchanger inhibitors

Sodium hydrogen exchanger (SHE) inhibitor is one of the most important targets in treatment of myocardial ischemia. In the course of our research into new types of non-acylguanidine, SHE inhibitory activities of 5-tetrahydroquinolinylidine aminoguanidine derivatives were used to build pharmacophore and 3D-QSAR models. Genetic Algorithm Similarity Program (GASP) was used to derive a 3D pharmacophore model which was used in effective alignment of data set. Eight molecules were selected on the basis of structure diversity to build 10 different pharmacophore models. Model 1 was considered as the best model as it has highest fitness score compared to other nine models. The obtained model contained two acceptor sites, two donor atoms and one hydrophobic region. Pharmacophore modeling was followed by substructure searching and virtual screening. The best CoMFA model, representing steric and electrostatic fields, obtained for 30 training set molecules was statistically significant with cross-validated coefficient (q(2)) of 0.673 and conventional coefficient (r(2)) of 0.988. In addition to steric and electrostatic fields observed in CoMFA, CoMSIA also represents hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. CoMSIA model was also significant with cross-validated coefficient (q(2)) and conventional coefficient (r(2)) of 0.636 and 0.986, respectively. Both models were validated by an external test set of eight compounds and gave satisfactory prediction (r(pred)(2)) of 0.772 and 0.701 for CoMFA and CoMSIA models, respectively. This pharmacophore based 3D-QSAR approach provides significant insights that can be used to design novel, potent and selective SHE inhibitors.     

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.     

Structure-activity relationships of a series of tariquidar analogs as multidrug resistance modulators

Tariquidar (XR9576) analogs, modulators of cancer multidrug resistance (MDR), were subjected to QSAR and 3D-QSAR analyses. The structural features contributing to anti-MDR activity were identified by the Free-Wilson analysis and pharmacophore search using Hoechst 33342 as a template. 3D-QSAR CoMFA and CoMSIA models were derived and tested. The best models yielded an external predictivity of 0.66-0.75 squared correlation coefficient and outlined HB-acceptor, steric, and hydrophobic fields as the most important 3D properties. On the basis of the QSAR and 3D-QSAR analyses it was suggested that the strong inhibitory potency of the compounds studied is related to the presence of a bulky aromatic ring system with a 3rd positioned heteroatom toward the anthranilamide nucleus in the opposite end of the tetrahydroquinoline group. The results can help in directing the rational design of new generations of potent P-glycoprotein MDR modulators.     

Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

Functional assay and structure-activity relationships of new third-generation P-glycoprotein inhibitors

Twenty-eight compounds, including 24 structurally related derivatives of tariquidar synthesized in our laboratory, and four XR compounds, reported by Xenova group Ltd, were investigated by the Hoechst 33342 and Calcein AM functional assays for estimation of their inhibitory effects on the transport activity of P-glycoprotein (P-gp). A high correlation between the effects obtained in both assays was observed at the substrate concentrations used. The analyses of kinetics data from experiments at different substrate concentrations revealed non-competitive inhibition in the Calcein AM assay and competitive inhibition in the Hoechst 33342 assay. The 3D structures of the compounds were further aligned on Hoechst 33342 using flexible and pharmacophore alignments. The results suggested that inhibitors could interact with the H-binding site of P-gp and this could potentially be achieved by different ways of binding. The best 3D-QSAR models, generated by CoMFA and CoMSIA, yielded an internal predictive squared correlation coefficient higher than 0.8 and included electrostatic, steric, hydrogen bond acceptor, and hydrophobic fields. Validation of the models on an external test set of 30 XR compounds gave predictive squared correlation coefficients of up to 0.66. An excellent correspondence between the experimental and modeled activities of the test compounds was observed. The models can be used for prediction and rational design of new P-gp inhibitors.     

Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

The 3D quantitative structure-activity relationships of 31 quinoline nuclei containing compounds and their biological activity have been investigated to establish various models. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies resulted in reliable and significant computational models. The obtained CoMFA model showed high predictive ability with q(2) = 0.592, r(2) = 0.966 and standard error of estimation (SEE) = 0.167, explaining majority of the variance in the data with two principal components. Predictions obtained with CoMSIA steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor fields (q(2) = 0.533, r(2) = 0.985) showed high prediction ability with minimum SEE (0.111) and four principal components. The information obtained from the CoMFA and CoMSIA contour maps can be utilized for the design and development of topoisomerase-II inhibitors for synthesis.     

Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Protein kinase B (PKB; also known as Akt kinase) is located downstream in the PI-3 kinase pathway. Overexpression and constitutive activation of PKB/Akt leads to human prostate, breast and ovarian carcinomas. A series of 69 PKB/Akt inhibitors were examined to explore their binding modes using FlexX, and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to provide structural insights into these compounds. CoMFA produced statistically significant results, with cross-validated q ² and non-cross validated correlation r ² coefficients of 0.53 and 0.95, respectively. For CoMSIA, steric, hydrophobic and hydrogen bond acceptor fields jointly yielded ‘leave one out’ q ²  = 0.51 and r ²  = 0.84. The predictive power of CoMFA and CoMSIA was determined using a test set of 13 molecules, which gave correlation coefficients, of 0.58 and 0.62, respectively. Molecular docking revealed that the binding modes of these molecules in the ATP binding sites of the Akt kinase domain were very similar to those of the co-crystallized ligand. The information obtained from 3D contour maps will allow the design of more potent and selective Akt kinase inhibitors.     

3D-QSAR CoMFA/CoMSIA studies on Urokinase plasminogen activator (uPA) inhibitors: a strategic design in novel anticancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of indole/benzoimidazole-5-carboxamidines as urokinase plasminogen activator (uPA) inhibitors. The ligand molecular superimposition on template structure was performed by atom/shape-based RMS fit, multifit, and RMSD fit methods. The removal of two outliers from the initial training set of 30 molecules improved the predictivity of the models. The statistically significant model was established from 28 molecules, which were validated by evaluation of test set of nine compounds. The atom-based RMS alignment yielded best predictive CoMFA model (r2(cv) = 0.611, r2(cnv) = 0.778, F value = 43.825, r2(bs) = 0.842, r2(pred) = 0.616 with two components) while the CoMSIA model yielded (r2(cv) = 0.499, r2(cnv) = 0.976, F value=96.36, r2(bs) = 0.993, r2(pred) = 0.694 with eight components). The contour maps obtained from 3D-QSAR studies were appraised for the activity trends of the molecules analyzed. The results indicate that the steric, electrostatic, and hydrogen bond donor/acceptor substituents play significant role in uPA activity and selectivity of these compounds. The data generated from the present study can be used as putative pharmacophore in the design of novel, potent, and selective urokinase plasminogen activator inhibitors as cancer therapeutics.     

Three-dimensional quantitative structure–farnesyltransferase inhibition analysis for some diaminobenzophenones

A 3D-QSAR investigation of 95 diaminobenzophenone yeast farnesyltransferase (FT) inhibitors selected from the work of Schlitzer et al. showed that steric, electrostatic, and hydrophobic properties play key roles in the bioactivity of the series. A CoMFA/CoMSIA combined model using the steric and electrostatic fields of CoMFA together with the hydrophobic field of CoMSIA showed significant improvement in prediction compared with the CoMFA steric and electrostatic fields model. The similarity of the 3D-QSAR field maps for yeast FT inhibition activity (from this work) and for antimalarial activity data (from previous work) and the correlation between those activities are discussed.     

Isolation,biological evaluation and 3D-QSAR studies of insecticidal/narcotic sesquiterpene polyol esters

For the first time, a set of (43) natural sesquiterpene polyol esters isolated from the root bark of Celastrus angulatus Maxim and Euonymus japonicus Thunb were subjected to 3D-QSAR comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies, with the aim of proposing novel sesquiterpene-based compounds with optimal narcotic or insecticidal activities. The established 3D-QSAR models exhibit reasonable statistical quality and prediction capabilities, with internal cross-validated Q ² values of ∼0.5 and external predicted R ² values of >0.9, respectively. The relative contributions of the steric/electrostatic fields of the 3D-QSAR models show that the electronic effect governs the narcotic activities of the molecules, but the hybrid effect of the electrostatic and hydrophobic interactions is more influential in the insecticidal activities of the compounds. These findings may have valuable implications for the development of novel natural insecticides.     

基于作用机制的氨基恶唑啉呫吨类BACE1抑制剂的分子设计

天冬氨酰蛋白酶(β-site amyloid precursor protein cleaving enzyme 1, BACE1)作为治疗阿尔兹海默症的潜在靶点,其抑制剂的开发已成为医学领域的重要研究方向。本文以59个氨基恶唑啉呫吨类BACE1抑制剂为研究对象,运用比较分子相似性指数(comparative molecular similarity index, CoMSIA)和分子对接方法,深入挖掘影响抑制剂活性的特征结构,以及抑制剂与BACE1间的结合模式和作用力类型,并以此为基础设计新型抑制剂并预测其活性。CoMSIA模拟结果表明,由立体场、静电场、疏水场和氢键供体场4个场组合建立的构效关系模型具有较强的预测能力,交叉验证相关系数Q2=0.48, 非交叉验证相关系数Rncv2=0.94, 外部预测相关系数Rpre2=0.85;通过分子对接,发现抑制剂占据了靶标的S3、S1和S2'位点,与BACE1之间的结合主要是通过氢键作用力和π-π堆积作用实现的;占据S2'位点的R取代基是立体场、静电场和疏水场影响的敏感区域,氨基恶唑啉核心官能团是氢键供体场的敏感区域。基于以上分析获得的抑制剂特征结构信息及其与蛋白质受体的作用机制,成功设计出了新的分子并预测了抑制活性。实验所得模型和信息,为后续新型BACE1抑制剂的结构优化和改造提供了重要理论依据     

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.     

3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach

3D-QSAR analysis has been performed on a series of previously synthesized benzonitrile derivatives, which were screened as farnesyltransferase inhibitors, using comparative molecular field analysis (CoMFA) with partial least-square fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 34 compounds. The predictive ability of the model developed was assessed using a test set of eight compounds (r(pred)(2) as high as 0.770). The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.991 and cross-validated coefficient q(2) value as 0.619. The analysis of CoMFA contour maps provided insight into the possible modification of the molecules for better activity.