Pharmacophore modeling studies on N-hydroxyphenyl acrylamides and N-hydroxypyridin-2-yl-acrylamides as inhibitor of human cancer leukemia K562 cells

In order to understand the essential structural features for inhibitors of human cancer leukemia K562 cells, three-dimensional pharmacophore hypotheses were built on the basis of a set of inhibitors of human cancer leukemia K562 selected from literature using PHASE program. Five point pharmacophore with two hydrogen bond acceptor (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophoric features were developed. Among them, the pharmacophore hypothesis AADRR 62 yielded a statistically significant 3D-QSAR model with as R ² value 0.883 and Q ² value 0.528 and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.765 was observed between experimental and predicted activity values of test set molecules.     

Computation of pharmacophore models for the prediction of mitogen-activated protein kinase activated protein kinase-2 inhibitory activity of pyrrolopyridines

This article is an attempt to formulate the three-dimensional pharmacophore modelling of pyrrolopyridine derivatives inhibiting mitogen-activated protein kinase activated protein kinase-2 (MK₂). To understand the essential structural features for MK₂ inhibitors, pharmacophore hypothesis were built on the basis of a set of known MK₂ inhibitors selected from literature using PHASE program. Three pharmacophore models with one hydrogen-bond acceptor (A), two hydrogen-bond donors (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis ADDHR1 yielded a statistically significant 3D-QSAR model with 0.926 as R ² value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.882 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore was expected to be useful for the design of selective MK₂ inhibitors.     

Pharmacophore development and docking studies of the hiv-1 integrase inhibitors derived from N-methylpyrimidones, Dihydroxypyrimidines, and bicyclic pyrimidinones

To elucidate the crucial structural features for the HIV-1 integrase inhibitors, a three-dimensional pharmacophore model was developed based on N-methyl pyrimidones, dihydroxypyrimidines, and bicyclic pyrimidinones derivatives using Phase. N-methyl pyrimidone derivative raltegravir, the first US-FDA approved drug by Merck, belongs to this series. The best-fitted common pharmacophore hypothesis was characterized by two acceptor, two hydrophobic, and two ring features having a correlation coefficient of 0.895, cross-validated Q(2) value of 0.631, and survival score of 8.862, suggesting that a highly predictive pharmacophore model was developed. The cross-validation studies using 23 test set molecules and fifteen structurally diverse HIV-integrase inhibitors give extra confidence about the correctness of the pharmacophore model. The cross-validation studies proved that our developed model can successfully differentiate between active and inactive HIV-integrase inhibitors. The docking studies were also carried out wherein the molecules were docked against the active site of HIV integrase to analyze the binding mode and the necessary structural requirement for their respective enzymatic inhibition. The results obtained from our studies provide a valuable tool for designing of new lead molecules with potent activity.     

Pharmacophore modeling and 3D QSAR analysis of isothiazolidinedione derivatives as PTP1B inhibitors

The article describes the development of a robust pharmacophore model and investigation of structure activity relationship analysis of 56 isothiazolidinedione derivatives reported as PTP1B inhibitors. A six-point pharmacophore model consisting of four aromatic rings (R), one hydrogen bond donor (D) and one hydrogen bond acceptor (A) with discrete geometries as pharmacophoric features was developed and the generated pharmacophore model was used to derive a predictive 3D QSAR model for the studied dataset. The obtained 3D QSAR model has an excellent correlation coefficient value (r ² = 0.98) along with good statistical significance as shown by a high Fisher ratio (F = 428.60). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (q ² = 0.62). The QSAR model suggests that hydrophobic aromatic character is crucial for the PTP1B inhibitory activity at the R-15 site.     

Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach

A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as Ki, of the training set molecules was from 0.3 to 11 300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.     

Pharmacophore modeling of some novel indole β-diketo acid and coumarin-based derivatives as HIV integrase inhibitors

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole β-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² = 0.81 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² = 0.69 for a randomly chosen test set of eight compounds. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may helps in the design and development of novel integrase inhibitors.     

Pharmacophore-based 3D-QSAR study of fungal chitin synthase inhibitors

To design new compounds with enhanced activity against the fungal chitin synthase enzyme, 3D-pharmacophore models were generated and QSAR study was carried out on 44 novel homoallylamines and related compounds, nikkomycin, maleimide, chalcones, and quinolin-2-one derivatives. A three-point pharmacophore with two hydrophobic (H) and one aromatic ring (R) as pharmacophore features was developed by PHASE module of Schrodinger molecular modeling suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² of 0.84 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² of 0.63 and Pearson-R value of 0.82 for a randomly chosen test set of nine compounds. The 3D-QSAR model explains the structure–activity relationship of these compounds which may help in the design and development of novel fungal chitin synthase inhibitors.     

作用于秋水仙碱结合位点的微管蛋白抑制剂药效团的构建

目的:构建作用于秋水仙碱结合位点的微管蛋白抑制剂药效团模型;初步分析该类抑制剂与靶点的作用方式。方法:使用Discovery Studio软件中的HypoGen模块对训练集进行药效团模型的构建。结果:最佳药效团模型的线性回归相关系数最高(0.981),包含1个氢键给体和4个疏水中心,利用测试集验证了该药效团模型的活性预测能力;通过分子与活性位点的对接得到了活性最好的两个化合物与此结合位点的具体作用方式。结论:得到的药效团模型具有较好的预测能力,有利于设计和开发新型微管蛋白抑制剂。     

Pharmacophore modeling for protein tyrosine phosphatase 1B inhibitors

A three dimensional chemical feature based pharmacophore model was developed for the inhibitors of protein tyrosine phosphatase 1B (PTP1B) using the CATALYST software, which would provide useful knowledge for performing virtual screening to identify new inhibitors targeted toward type II diabetes and obesity. A dataset of 27 inhibitors, with diverse structural properties, and activities ranging from 0.026 to 600 microM, was selected as a training set. Hypol, the most reliable quantitative four featured pharmacophore hypothesis, was generated from a training set composed of compounds with two H-bond acceptors, one hydrophobic aromatic and one ring aromatic features. It has a correlation coefficient, RMSD and cost difference (null cost-total cost) of 0.946, 0.840 and 65.731, respectively. The best hypothesis (Hypol) was validated using four different methods. Firstly, a cross validation was performed by randomizing the data using the Cat-Scramble technique. The results confirmed that the pharmacophore models generated from the training set were valid. Secondly, a test set of 281 molecules was scored, with a correlation of 0.882 obtained between the experimental and predicted activities. Hypol performed well in correctly discriminating the active and inactive molecules. Thirdly, the model was investigated by mapping on two PTP1B inhibitors identified by different pharmaceutical companies. The Hypol model correctly predicted these compounds as being highly active. Finally, docking simulations were performed on few compounds to substantiate the role of the pharmacophore features at the binding site of the protein by analyzing their binding conformations. These multiple validation approaches provided confidence in the utility of this pharmacophore model as a 3D query for virtual screening to retrieve new chemical entities showing potential as potent PTP1B inhibitors.     

A combination of pharmacophore modeling,virtual screening,and molecular docking studies for a diverse set of colchicine site inhibitors

In the present study, 3D QSAR pharmacophore models were undertaken from a set of 16 colchicine binding site inhibitors (CSIs). The best pharmacophore model possessing two chemical features (hydrogen-bond acceptor and hydrophobic) showed an excellent correlation coefficient for the training ( \(r_{\text{training}} = 0.96\) ) and a fair correlation coefficient for the test set ( \(r_{\text{test}} = 0.88\) ) molecules. Considering the statistically significant results of the best pharmacophore model, the hypothesis was selected as a 3D structural query to screen the Maybridge and MiniMaybridge database. Ultimately, three of the hit molecules satisfied all of these conditions, which were then submitted to molecular docking studies to evaluate their optimal orientations and their interactions with the critical residues of tubulin. The molecules showed strong hydrogen-bond interactions as well as hydrophobic contacts with critical residues such as β:Tyr376, β:Ile378, Tyr48, and Ser178. As indicated above, the hit three molecules can be good candidates for the CSIs. And the developed pharmacophore model can be used to determine the essential structural requirements, thus as a valuable tool to select the novel compounds based on virtual screening approach.     

Pharmacophore generation and atom-based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors

Background

Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. Herein, we describe the development of effective and robust pharmacophore model and the structure–activity relationship studies of 43N-iso-propyl pyrrole-based derivatives previously reported for HMG-CoA reductase inhibition.

Results

A 5-point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D quantitative structure–activity relationship analysis (3D-QSAR) model for the studied dataset. The obtained 3D-QSAR model has an excellent correlation coefficient value (r ²?=?0.96) along with good statistical significance as shown by high Fisher ratio (F?=?143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q ²?=?0.672). Further, pharmacophoric model was employed for virtual screening to identify four potential HMG-CoA reductase inhibitors.

Conclusions

The QSAR model suggests that electron-withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron-withdrawing character, hydrogen bond--donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.     

Generation of predictive pharmacophore models for CCR5 antagonists: study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors

Predictive pharmacophore models were developed for a large series of piperidine- and piperazine-based CCR5 antagonists as anti-HIV-1 agents reported by Schering-Plough Research Institute in recent years. The pharmacophore models were generated using a training set consisting of 25 carefully selected antagonists based on well documented criteria. The activity spread, expressed in K(i), of training set molecules was from 0.1 to 1300 nM. The most predictive pharmacophore model (hypothesis 1), consisting of five features, namely, two hydrogen bond acceptors and three hydrophobic, had a correlation (r) of 0.920 and a root mean square of 0.879, and the cost difference between null cost and fixed cost was 44.46 bits. The model was cross-validated by randomizing the data using the CatScramble technique. The results confirmed that the pharmacophore models generated from the test set were not due to chance correlation. The best model (hypothesis 1) was validated using test set molecules (total of 78) and performed well in classifying active and inactive molecules correctly. The model was further validated by mapping onto it a diverse set of six CCR5 antagonists identified by five different pharmaceutical companies. The best model correctly predicted these compounds as being highly active. These multiple validation approaches provide confidence in the utility of the predictive pharmacophore model developed in this study as a 3D query tool in virtual screening to retrieve new chemical entities as potent CCR5 antagonists. The model can also be used in predicting biological activities of compounds prior to undertaking their costly synthesis.     

In silico accounting of novel pyridazine analogues as h-PTP 1B inhibitors: pharmacophore modelling,atom-based 3D QSAR and docking studies

PTP 1B, a negative regulator of insulin signalling pathway, has been rigorously investigated for its potential for design and development of drugs for the management of type 2 diabetes. Pharmacophore modelling, atom-based 3D QSAR and docking studies were performed on a series of 37 pyridazine derivatives reported as PTP 1B inhibitors. A five-point pharmacophore model consisting of two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic (H) and one aromatic ring (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The robustness of the generated model was validated by good correlation coefficient value (r ² = 0.832), Pearson-R value (0.8593), cross-validated correlation coefficient (q ² = 0.663). This study investigated some of the indispensible structural features of pyridazine analogues which can further be exploited to optimize h-PTP 1B inhibitors.     

Virtual screening of cathepsin k inhibitors using docking and pharmacophore models

Cathepsin K is a lysosomal cysteine protease that is highly and selectively expressed in osteoclasts, the cells which degrade bone during the continuous cycle of bone degradation and formation. Inhibition of cathepsin K represents a potential therapeutic approach for diseases characterized by excessive bone resorption such as osteoporosis. In order to elucidate the essential structural features for cathepsin K, a three-dimensional pharmacophore hypotheses were built on the basis of a set of known cathepsin K inhibitors selected from the literature using catalyst program. Several methods are used in validation of pharmacophore hypothesis were presented, and the fourth hypothesis (Hypo4) was considered to be the best pharmacophore hypothesis which has a correlation coefficient of 0.944 with training set and has high prediction of activity for a set of 30 test molecules with correlation of 0.909. The model (Hypo4) was then employed as 3D search query to screen the Maybridge database containing 59,000 compounds, to discover novel and highly potent ligands. For analyzing intermolecular interactions between protein and ligand, all the molecules were docked using Glide software. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme inhibitor interaction pattern identified from molecular docking.     

Targeting Pim-1 kinase for potential drug-development

Dysregulation of Pim-1 kinase has been implicated in several human cancers. Many potential inhibitors of PIM kinase have been reported, but potential bioactive compounds are still far from reach. Keeping this in mind, we have selected structurally known diverse Pim-1 kinase inhibitors to find novel small molecule drug-leads. A ligand-based pharmacophore model for Pim-1 kinase was developed using PHASE software. A four feature pharmacophoric hypothesis (AAHR) was used to develop atom-based 3D-QSAR model with the best regression coefficient of 0.9433 and Pearson-R of 0.9344. Compounds from Asinex platinum database were obtained whose pIC(50) values matched the 3D-QSAR model. Structural and molecular interaction studies on the training and test sets suggest that designing novel compounds hydrogen bond with Asp128 in the bioactive region of Pim-1 kinase would result in therapeutic success.     

Pharmacophore modeling and density functional theory analysis for a series of nitroimidazole compounds with antitubercular activity

In an attempt to highlight structural features required for potent antitubercular activity, five pharmacophoric features were developed for PA-824 and its analogs. The generated pharmacophore indicated importance of a nitro group, three hydrogen bond acceptor features, and a distal aromatic ring for potent activity. The model based on pharmacophore alignment has good correlation coefficient for the training set (r(2) = 0.81, SD = 0.31, F = 122.9, N = 152), which was evaluated using a test set (Q(2) = 0.77, root-mean-square error = 0.35, Pearson-R = 0.88, N = 49). Structure-activity relationship investigation further revealed that hydrophobic substitutions at the para-position of distal aromatic ring could lead to more potent analogs. The most active and inactive compounds were further studied using density functional theory at B3LYP/3-21*G level. The calculated electrostatic profile indicated that these compounds possess maximum negative potential in the vicinity of nitro group extending laterally to the imidazole ring. Furthermore, the calculated electron affinity values indicate the stability of radical anions, which could form upon one electron reduction in the biological system, thus, indicating the electron acceptor capacity of these compounds. Results of this study are expected to be useful in the design of novel potent nitroimidazoles as antitubercular agents.     

A paradigm for development of novel PTP 1B inhibitors: Pharmacophore modelling, atom-based 3D-QSAR and docking studies

Inhibitors of protein tyrosine phosphatase 1B (PTP 1B) are of great interest for the development of newer therapeutics for the management of type 2 diabetes mellitus (T2DM). In order to understand structural requirement of molecules to act as PTP 1B inhibitors, ligand-based pharmacophore model, atom-based 3D-QSAR and structure-based drug design studies have been performed on a series of thiophene derivatives to correlate their molecular architecture with PTP 1B inhibitory activity. A five-point pharmacophore hypothesis with one hydrogen acceptor (A), two negative ionic (N), and two aromatic rings (R) as pharmacophoric features were developed using PHASE module of Schrödinger suite. The pharmacophore hypothesis was characterized by good PLS statistics (survival score = 3.894, the best cross validated r ² (Q ²) = 0.737, regression coefficient r ² = 0.968, Pearson-R = 0.887 and F value = 253.3). Docking studies demonstrated binding orientations of ligands in dataset with bidentate binding pockets of the enzyme and importance of hydrophobic groups. Taken together, Partial least square (PLS) generated 3D-QSAR pharmacophore and regression cubes along with structure based drug design provided a three dimensional topological view of active site that can be used for rational modification of ligands for optimal PTP 1B inhibitory activity.     

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology

The present study describes ligand‐based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross‐validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds ( 47 and 48 ; predicted IC₅₀ = 1 nm ) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 μg/mL) with IC₅₀ value of 3.6 and 2.5 nm , respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.     

Pharmacophore generation and atom-based 3D-QSAR of

ABSTRACT: BACKGROUND: Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. METHODS: This article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 43 N-iso-propyl pyrrole-based derivatives reported for HMG-CoA reductase inhibition. A five point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D-QSAR model for the studied dataset. RESULTS: The obtained 3D-QSAR model has an excellent correlation coefficient value (r2 = 0.9566) along with good statistical significance as shown by high Fisher ratio (F = 143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q2 = 0.6719). CONCLUSIONS: The QSAR model suggests that electron withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron withdrawing character, hydrogen bond donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.