Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors

Cyclooxygenase-2 (COX-2) inhibitors are widely used for the treatment of pain and inflammatory disorders such as rheumatoid arthritis and osteoarthritis. A series of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives has been reported as COX-2 inhibitors. In order to understand the structural requirement of these COX-2 inhibitors, a ligand-based pharmacophore and atom-based 3D-QSAR model have been developed. A five-point pharmacophore with four hydrogen bond acceptors (A) and one hydrogen bond donor (D) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The training set correlation is characterized by PLS factors (r ² = 0.642, SD = 0.65, F = 82.7, P = 7.617 e − 12). The test set correlation is characterized by PLS factors (Q ² _ext = 0.841, RMSE = 0.24,Pearson−R = 0.91). A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Arg513, Val523, Phe518, Ser530, Tyr355, His90) of COX-2 enzyme. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR give detailed structural insights as well as highlights important binding features of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives as COX-2 inhibitors which can provide guidance for the rational design of novel potent COX-2 inhibitors.     

吡啶并嘧啶类Wee1激酶衍生物的三维定量构效关系与分子对接

采用比较分子场分析方法(CoMFA)和比较分子相似性指数分析方法(CoMSIA)对一系列吡啶并嘧啶类衍生物进行了三维定量构效关系(3D-QSAR)研究,建立了CoMFA和CoMSIA两种模型. 所构建的最佳模型的交叉验证相关系数分别为0.707和0.645,非交叉验证系数分别是0.964和0.972,模型的一些外部验证表明两个模型合理、可靠,并具有良好的预报能力. 同时,用分子对接的方法分析了该类化合物与Wee1激酶结构的作用模式,结果进一步表明,在R1和R5取代基上引入正电性基团,R2为体积小的电负性基团,同时选择体积中等和强的推电子的R3但亲水性的X取代基,能有效改善这类化合物的抑制活性.     

De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction

Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q ², 0.671; r ², 0.969; CoMSIA with q ², 0.608; r ², 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein?Cligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of hydroxyethylamine derivatives as selective human BACE-1 inhibitor

Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), on a series of 43 hydroxyethylamine derivatives, acting as potent inhibitors of β-site amyloid precursor protein (APP) cleavage enzyme (BACE-1). The crystal structure of the BACE-1 enzyme (PDB ID: 2HM1) with one of the most active compound 28 was available, and we assumed it to be the bioactive conformation of the studied series, for 3D-QSAR analysis. Statistically significant 3D-QSAR model was established on a training set of 34 compounds, which were validated by a test set of 9 compounds. For the best CoMFA model, the statistics are, r ² =  0.998, r²_cv = 0.810{r^{2}_{\rm cv} = 0.810} , n =  34 for the training set and r²_pred = 0.934{r^{2}_{\rm pred} = 0.934} , n = 9 for the test set. For the best CoMSIA model (combined steric, electrostatic, hydrophobic, and hydrogen bond donor fields), the statistics are r ² =  0.978, r²_cv = 0.754{r^{2}_{\rm cv} = 0.754} , n =  34 for the training set and r²_pred = 0.750{r^{2}_{\rm pred} = 0.750} , n =  9 for the test set. 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. The data generated from the present study will further help to design novel, potent, and selective BACE-1 inhibitors.     

A combined 3D-QSAR and molecular docking strategy to understand the binding mechanism of V600EB-RAF inhibitors

B-RAF is a member of the RAF protein kinase family involved in the regulation of cell growth, differentiation, and proliferation. It forms a part of conserved apoptosis signals through the RAS?CRAF?CMAPK pathway. ^V600EB-RAF protein has much potential for scientific research as therapeutic target due to its involvement in human melanoma cancer. In this work, a molecular modeling study was carried out for the first time with 3D-QSAR studies by following the docking protocol on three different data sets of ^V600EB-RAF inhibitors. Based on the co-crystallized compound (PDB ID: 1UWJ), a receptor-guided alignment method was utilized to derive reliable CoMFA and CoMSIA models. The selected CoMFA model gives the best statistical values (q ² =?0.753, r ² =?0.962). With the same alignment protocol, a statistically reliable CoMSIA model out of fourteen different combinations was also derived (q ² = 0.807, r ² = 0.961). The actual predictive powers of both models were rigorously validated with an external test set, which gave satisfactory predictive r ² values for CoMFA and CoMSIA models, 0.89 and 0.88, respectively. In addition, y-randomization test was also performed to validate our 3D-QSAR models. Contour maps from CoMFA and CoMSIA models supported statistical results, revealed important structural features responsible for biological activity within the active site and explained the correlation between biological activity and receptor?Cligand interactions. Based on the developed models few new structures were designed. The newly predicted structure (IIIa) showed higher inhibitory potency (pIC₅₀ 6.826) than that of the most active compound of the series.     

CoMFA and CoMSIA of diverse pyrrolidine analogues as dipeptidyl peptidase IV inhibitors: active site requirements

The inhibition of dipeptidyl peptidase IV (DPP-IV) has emerged as an attractive target in the treatment of type 2 diabetes. In view of this development, a critical analysis of structural requirements of the DPP-IV inhibitors is envisioned to identify the significant features toward design of selective inhibitors. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) contour plots of pyrrolidine based analogues are used to analyze the structural requirements of a DPP-IV active site. The CoMFA model has shown a cross-validated q ² of 0.651 with a non-cross-validated r ² of 0.882 and explained 70.6% variance in the activity of external test compounds. In this, the steric and electrostatic fields have respectively contributed 59.8 and 40.2%, respectively, to the explained activity of the compounds. The CoMSIA model has shown optimum predictivity (cross-validated q ² = 0.661; non-cross-validated r ² = 0.803; external test set’s predictive r ² = 0.706) with four molecular fields namely, steric, electrostatic, hydrogen bond (HB)-donor, and HB-acceptor. The contour plots of molecular fields resulting from these studies have suggested: (i) steric restriction with small electron rich substituent at 2- and 3-position of pyrrolidine ring, (ii) presence of electropositive ring linker between the pyrrolidine head and aryl tail, (iii) presence of electron-rich groups around the aryl tail moiety, and (iv) presence of sulfonamide between the ring linker and aryl tail which would increase DPP-IV binding affinity of the compounds. These findings will help in the design of structurally related/new compounds as potential DPP-IV inhibitors.     

Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study

The present study describes a systematic 3D-QSAR study consisting of pharmacophore modeling, docking, and integration of ligand-based and structure-based drug design approaches, applied on a dataset of 72 Hsp90 inhibitors as anti-cancer agents. The best pharmacophore model, with one H-bond donor (HBD), one H-bond acceptor (HBA), one hydrophobic_aromatic (Hy_Ar), and two hydrophobic_aliphatic (Hy_Al) features, was developed using the Catalyst/HypoGen algorithm on a training set of 35 compounds. The model was further validated using test set, external set, Fisher’s randomization method, and ability of the pharmacophoric features to complement the active site amino acids. Docking analysis was performed using Hsp90 chaperone (PDB-Id: 1uyf) along with water molecules reported to be crucial for binding and catalysis (Sgobba et al. ChemMedChem 4:1399–1409, 2009). Furthermore, an integration of the ligand-based as well as structure-based drug design approaches was done leading to the integrated model, which was found to be superior over the best pharmacophore model in terms of its predictive ability on internal [integrated model 2: R _(train) = 0.954, R _(test) = 0.888; Hypo-01: R _(train) = 0.912 and R _(test) = 0.819] as well as on external data set [integrated model 2: R _(ext.set) = 0.801; Hypo-01: R _(ext.set) = 0.604].     

Molecular dynamics directed CoMFA studies on carbocyclic neuraminidase inhibitors

Zanamivir is the known potent anti-influenza agent targeting the key enzyme neuraminidase that cleaves sialic acid from cell receptors allowing release of newly formed virions. Molecular dynamics simulation was carried out to determine the dynamic behavior of Zanamivir upon its binding to flexible loops of neuraminidase and to analyse its interactions in the bioactive state. Neuraminidase exhibits wide range of affinity with structurally similar compounds. CoMFA study was used to determine quantitative structure-activity relationship for 36 carbocyclic Neuraminidase inhibitors (NIs). The CoMFA model was also successfully built using cross-validated r²_cv = 0.580{{r}^{2}_{\rm cv} =0.580} and r²_pred=0.638{{r}^{2}_{\rm pred}=0.638} .     

Molecular Modeling and Design of Arylthioindole Derivatives as Tubulin Inhibitors

对一系列具有抗人乳腺癌细胞系MCF-7生物活性的微管蛋白抑制剂─芳基硫代吲哚衍生物(arylthioindole),进行了三维定量构效关系(3D-QSAR)和对接(docking)研究. 在训练集中,建立了具有良好统计质量和预报能力的比较分子力场分析(CoMFA) 模型,其非交叉验证相关系数平方R₂为0.898,交叉验证相关系数平方q₂为0.654. 同时在测试集的验证中得到预测相关系数平方R₂(pred)为0.816, 进一步表明了该模型具有较高的预测能力. 此外,通过对接研究,获得了这些化合物与微管蛋白作用的键合方式和构象,发现该系列化合物的CoMFA力场分布与对接结合位点上的三维拓朴结构相一致. 根据CoMFA和对接分析的结果,细致地讨论和总结了有利于提高或改进该类化合物活性的主要因素,即在取代基R3、R4和R5上引进高电负性的基团,在取代基R6上引进带有高电负性且大体积的基团,以及在取代基R7上引进小体积的基团等都是有利的. 基于这些研究结果,在理论上还设计了5个新的具有较高活性的化合物.     

Pharmacophore mapping of the crucial mediators of acetylcholinesterase and butyrylcholinesterase dual inhibition in Alzheimer's disease

Optimization and re-optimization of bioactive molecules using in silico methods have found application in the design of more active ones. Herein, we applied a pharmacophore modeling approach to screen potent dual inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) aimed at Alzheimer's disease (AD) treatment. The investigation entails molecular dynamics simulation, docking, pharmacophore modeling, drug-like screening, and binding energy analysis. We prepared a pharmacophore model from approved inhibitors of AChE and BuChE to predict the crucial moieties required for optimum molecular interaction with these proteins. The obtained pharmacophore model, used for database screening via some critical criteria, showed 229 hit molecules. Further analyses showed 42 likely dual inhibitors of AChE/BuChE with drug-like and pharmacokinetics properties the same as the approved cholinesterase inhibitors. Finally, we identified 14 dual molecules with improved potentials over the existing inhibitors and simulated ZINC92385797 bound to human AChE and BuChE structure after noticing that these 14 molecules are similar. The selected compound maintained relative stability at the active sites of both proteins over 120 ns simulation. Our integrated protocols showed the pertinent recipes of anti-AD drug design through the in silico pipeline.

Graphical abstract

     

Molecular modeling studies, synthesis, and biological evaluation of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR) inhibitors

The search for new antimalarial agents is necessary as current drugs in the market become vulnerable due to the emergence of resistance strains of Plasmodium falciparum (P. falciparum). The biosynthetic pathway for fatty acids has been recognized and validated as an important drug target in P.falciparum. One of the important enzymes in this pathway that has a determinant role in completing the cycles of chain elongation is Enoyl-ACP reductase (ENR) also popularly known as FabI. In this paper we report the design, synthesis, and microbial evaluation of inhibitors of Plasmodium enoyl reductase (PfENR). The search for inhibitors involved a virtual screening of the iResearch database with docking simulations. One of the hits was selected and modified to optimize its binding to PfENR; this resulted in the development of analogues of N-benzylidene-4-phenyl-1,3-thiazol-2-amine. The activity of these analogues was predicted from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models constructed from a dataset of 43 known inhibitors of PfENR. The most promising molecules were synthesized and their structures characterized by spectroscopic techniques. The molecules were screened for in vitro antimalarial activity by whole-cell assay method. Two molecules, viz. VRC-007 and VRC-009, were found to be active at 4.67 and 7.01 μM concentrations, respectively.     

The Numerical Methods for Oscillating Singularities in Elliptic Boundary Value Problems

The singularities near the crack tips of homogeneous materials are monotone of type r^α and r^α log^δr (depending on the boundary conditions along nonsmooth domains). However, the singularities around the interfacial cracks of the heterogeneous bimaterials are oscillatory of type r^α sin( log r). The method of auxiliary mapping (MAM), introduced by Babu ka and Oh, was proven to be successful in dealing with r^α type singularities. However, the effectiveness of MAM is reduced in handling oscillating singularities. This paper deals with oscillating singularities as well as the monotone singularities by extending MAM through introducing the power auxiliary mapping and the exponential auxiliary mapping.     

Identification of dual Acetyl-CoA carboxylases 1 and 2 inhibitors by pharmacophore based virtual screening and molecular docking approach

Acetyl-CoA carboxylase (ACC) is a crucial metabolic enzyme that plays a vital role in obesity-induced type 2 diabetes and fatty acid metabolism. To identify dual inhibitors of Acetyl-CoA carboxylase1 and Acetyl-CoA carboxylase2, a pharmacophore modelling approach has been employed. The best HypoGen pharmacophore model for ACC2 inhibitors (Hypo1_ACC2) consists of one hydrogen bond acceptor, one hydrophobic aliphatic and one hydrophobic aromatic feature, whereas the best pharmacophore (Hypo1_ACC1) for ACC1 consists of one additional hydrogen-bond donor (HBD) features. The best pharmacophore hypotheses were validated by various methods such as test set, decoy set and Cat-Scramble methodology. The validated pharmacophore models were used to screen several small-molecule databases, including Specs, NCI, ChemDiv and Natural product databases to identify the potential dual ACC inhibitors. The virtual hits were then subjected to several filters such as estimated $\text{ IC}_{50}$ value, quantitative estimation of drug-likeness and molecular docking analysis. Finally, three novel compounds with diverse scaffolds were selected as potential starting points for the design of novel dual ACC inhibitors.     

Synthesis, in vitro antitubercular activity and 3D-QSAR study of 1,4-dihydropyridines

In continuation of our research program on new antitubercular agents, this article is a report of the synthesis of 97 various symmetrical, unsymmetrical, and N-substituted 1,4-dihydropyridines. The synthesized molecules were tested for their activity against M. tuberculosis H ₃₇Rv strain with rifampin as the standard drug. The percentage inhibition was found in the range 3–93%. In an effort to understand the relationship between structure and activity, 3D-QSAR studies were also carried out on a subset that is representative of the molecules synthesized. For the generation of the QSAR models, a training set of 35 diverse molecules representing the synthesized molecules was utilized. The molecules were aligned using the atom-fit technique. The CoMFA and CoMSIA models generated on the molecules aligned by the atom-fit method show a correlation coefficient (r ²) of 0.98 and 0.95 with cross-validated r ²(q ²) of 0.56 and 0.62, respectively. The 3D-QSAR models were externally validated against a test set of 19 molecules (aligned previously with the training set) for which the predictive ${r^{2} (r^{2}_{\rm pred})}$ is recorded as 0.74 and 0.69 for the CoMFA and CoMSIA models, respectively. The models were checked for chance correlation through y-scrambling. The QSAR models revealed the importance of the conformational flexibility of the substituents in antitubercular activity.     

Identification of new novel scaffold for Aurora A inhibition by pharmacophore modeling and virtual screening

Aurora kinases belong to family of highly conserved serine/threonine protein kinases that are involved in diverse cell cycle events and play a major role in regulation of cell division. Abnormal expression of Aurora kinases may lead to cancer; hence, these are considered as a potential target in cancer treatment. In this research article, we identified three novel Aurora A inhibitors using modern computational tools. A four-point common 3D pharmacophore hypothesis of Aurora A (AurA) inhibitors was developed using a diverse set of 55 thienopyrimidine derivatives. A three-dimensional quantitative structure–activity relationship (3D-QSAR) study was carried out using atom-based alignment of diverse set of 55 molecules to evaluate the structure– activity relationships. Docking and 3D-QSAR studies were performed with the 3D structure of AurA to evaluate the generated pharmacophore. The pharmacophore model and 3D-QSAR results complemented the results of our docking study. The pharmacophore hypothesis, which yields the best results, was used to screen the Zinc ‘clean drug-like’ database. Various database filters such as 3D-arrangement of pharmacophoric features, predicted activity and binding interaction score were used to retrieve hits having potential AurA inhibition activity.     

3D-QSAR,molecular dynamics simulations,and molecular docking studies on pyridoaminotropanes and tetrahydroquinazoline as mTOR inhibitors

Cancer is a second major disease after metabolic disorders where the number of cases of death is increasing gradually. Mammalian target of rapamycin (mTOR) is one of the most important targets for treatment of cancer, specifically for breast and lung cancer. In the present research work, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies were performed on 50 compounds reported as mTOR inhibitors. Three different alignment methods were used, and among them, distill method was found to be the best method. In CoMFA, leave-one-out cross-validated coefficients \((q^{2})\), conventional coefficient \((r^{2})\), and predicted correlation coefficient \((r^{2}_{\mathrm{pred}})\) values were found to be 0.664, 0.992, and 0.652, respectively. CoMSIA study was performed in 25 different combinations of features, such as steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic. From this, a combination of steric, electrostatic, hydrophobic (SEH), and a combination of steric, electrostatic, hydrophobic, donor, and acceptor (SEHDA) were found as best combinations. In CoMSIA (SEHDA), \(q^{2}\), \(r^{2}\) and \(r^{2}_{\mathrm{pred}}\) were found to be 0.646, 0.977, and 0.682, respectively, while in the case of CoMSIA (SEH), the values were 0.739, 0.976, and 0.779, respectively. Contour maps were generated and validated by molecular dynamics simulation-assisted molecular docking study. Highest active compound 19, moderate active compound 15, and lowest active compound 42 were docked on mTOR protein to validate the results of our molecular docking study. The result of the molecular docking study of highest active compound 19 is in line with the outcomes generated by contour maps. Based on the features obtained through this study, six novel mTOR inhibitors were designed and docked. This study could be useful for designing novel molecules with increased anticancer activity.