Pharmacophore modeling and virtual screening for the discovery of new fatty acid amide hydrolase inhibitors

A predictive pharmacophore model has been generated from a series of diverse fatty acid amide hydrolase (FAAH) inhibitors and the optimal pharmacophore model applied in virtual screening. The pharmacophore model was based on a training set of 21 compounds carefully selected from the published literatures. The optimal model Hypo-1 included four features (two hydrogen-bond acceptor units, one aromatic hydrophobic unit and one aromatic ring unit) and two excluded volumes. Cross-validation of the model confirmed that Hypo-1 was not generated by chance correlation. A large test set of 55 compounds showed that Hypo-1 performed well in classifying highly active and less active FAAH inhibitors. Superimposition analysis of the FAAH X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the model. Virtual screening generated a total of 976 hits from the Zinc Natural Products database, a hit rate of 1.04% and enrichment of 83.89. The acceptable hit rate further supports the use of Hypo-1 as a 3D query tool for virtual screening.     

Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model

Considerable effort has been devoted to the characterization of P-glycoprotein - drug interaction in the past. Systematic quantitative structure-activity relationship (QSAR) studies identified both predictive physicochemical parameters and pharmacophoric substructures within homologous series of compounds. Comparative molecular field analysis (CoMFA) led to distinct 3D-QSAR models for propafenone and phenothiazine analogs. Recently, several pharmacophore models have been generated for diverse sets of ligands. Starting from a training set of 15 propafenone-type MDR-modulators, we established a chemical function-based pharmacophore model. The pharmacophoric features identified by this model were (i) one hydrogen bond acceptor, (ii) one hydrophobic area, (iii) two aromatic hydrophobic areas, and (iv) one positive ionizable group. In silico screening of the Derwent World Drug Index using the model led to identification of 28 compounds. Substances retrieved by database screening are diverse in structure and include dihydropyridines, chloroquine analogs, phenothiazines, and terfenadine. On the basis of its general applicability, the presented 3DQSAR model allows in silico screening of virtual compound libraries to identify new potential lead compounds.     

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.     

A three-dimensional in silico pharmacophore model for inhibition of Plasmodium falciparum cyclin-dependent kinases and discovery of different classes of novel Pfmrk specific inhibitors

The cell division cycle is regulated by a family of cyclin-dependent protein kinases (CDKs) that are functionally conserved among many eukaryotic species. The characterization of plasmodial CDKs has identified them as a leading antimalarial drug target in our laboratory. We have developed a three-dimensional QSAR pharmacophore model for inhibition of a Plasmodium falciparum CDK, known as Pfmrk, from a set of fifteen structurally diverse kinase inhibitors with a wide range of activity. The model was found to contain two hydrogen bond acceptor functions and two hydrophobic sites including one aromatic-ring hydrophobic site. Although the model was not developed from X-ray structural analysis of the known CDK2 structure, it is consistent with the structure-functional requirements for binding of the CDK inhibitors in the ATP binding pocket. Using the model as a template, a search of the in-house three-dimensional multiconformer database resulted in the discovery of sixteen potent Pfmrk inhibitors. The predicted inhibitory activities of some of these Pfmrk inhibitors from the molecular model agree exceptionally well with the experimental inhibitory values from the in vitro CDK assay.     

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.     

Pharmacophore modeling for the identification of small-molecule inhibitors of TACE

OBJECTIVE To employ pharmacophore modeling to identify a TACE inhibitor from an inhouse database of 66 organic compounds.METHODS To identify the common features required for TACE inhibition,we generated a pharmacophore model from a set of TACE-selective inhibitor using the Common Feature Pharmacophore Model protocol implemented in Discovery Studio 3.1.1.A fluorimetric assay was used to investigate the potential ability of compounds to inhibit TACE enzymatic activity.The ability of compound 1 to inhibit TACE activity in a human monocyte THP-1 cell line was evaluated by ELISA.RESULTS In this study,apharmacophore model constructed from a training set of TACE inhibitors was used to screen an in-house database of organic compounds,from which compound 1 emerged as a top candidate.In a cell-free assay,compound 1inhibited TACE enzymatic activity in a dose-dependent manner.Moreover,compound 1 inhibited the production of soluble TNF-αin human acute monocytic leukemia THP-1 cells without impacting nitric oxide production,and exhibited anti-proliferative activity against THP-1cells.CONCLUSION Compound 1 was found to inhibit TACE enzymatic activity in a cell-free system and LPS-induced TNF-αsecretion in cellulo.We envisage that compound 1 may be employed as a useful scaffold for the development of more potent TACE 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.     

Tubulin inhibitors： pharmacophore modeling,virtua screening and molecular docking

Aim： To construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities. Methods： Ligand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment （MOE） software and to biological evaluation in vitro. Results： Hypol was demonstrated to be the best pharmacophore model that exhibited the highest correlation coefficient （0.9582）, largest cost difference （70.905） and lowest RMSD value （0.6977）. Hypol consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypol was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypol was used in virtual screening of the Specs database, 952 drug-like compounds were revealed. After docking into the colchicine-binding site of tubulin, 5 drug-like compounds with the required interaction with the critical amino acid residues and the binding free energies 〈-4 kcal/mol were selected as representative leads. Compounds 1 and 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro. Conclusion： Hypol is a quantitative pharmacophore model for tubulin inhibitors, which not only provides a better understanding of their interaction with tubulin, but also assists in discovering new potential leads with antitumor activities.     

3D-QSAR and docking studies of benzoyl urea derivatives as tubulin-binding agents for antiproliferative activity

Tubulins, an αβ heterodimers, the major component of microtubules, are important molecular target of numerous small molecule ligands for anticancer therapy. In this study, the molecular modeling studies were performed to develop predictive 3D-QSAR models using set of 32 compounds of benzoyl urea derivatives as tubulin-binding agents for antiproliferative activity. A five-point common pharmacophore hypotheses with one hydrogen bond acceptor (A), two hydrogen bond donors (D), one hydrophobic (H), and one ring (R) vector features were selected for alignment of all compounds. The 3D-QSAR models generated using training set of 21 compounds and test set of 11 compounds showed good partial least squares statistical results. The developed CPHs and 3D-QSAR models were validated further externally by predicting the activity of database of compounds from literature and comparing it with actual activity. Docking studies were also carried out for all compounds on colchicine-binding site of β-tubulin for studying of binding affinity of compounds for antiproliferative activity. The results of these molecular modeling studies are helpful to refine the pharmacophore for design of new potential compounds for antiproliferative activity.     

Atom based 3D-QSAR study of 1,4-benzodiazepine-2-ones as potent anti-trypanosomal agents and its validation

In the investigation, a robust pharmacophore model to investigate structure–activity relationship of 1,4-benzodiazepine-2-ones derivatives was developed for human African trypanosomiasis (HAT) using PHASE module of Schrodinger software. The pharmacophore model consists of five contours such as two aromatic rings (R), one hydrophobic hydrogen (H) and two hydrogen-bond acceptor (A) with discrete geometries. The generated pharmacophore model was used to derive a predictive atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) model for the studied data set. The obtained 3D-QSAR model has an excellent correlation coefficient value (R ² = 0.97) along with good statistical significance as shown by high Fisher ratio (F = 216.80). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (Q ² = 0.80). To confirm the validity of the model further calculation of the enrichment factor and percentage recovery studies were calculated, which confirm the validity of the model. The findings of the QSAR study provide a set of guidelines for designing compounds with better HAT inhibitory activity.     

Derivation of pharmacophore and CoMFA models for leukotriene D(4) receptor antagonists of the quinolinyl(bridged)aryl series

The present work focuses on the study of the three-dimensional (3D) structural requirements for the leukotriene D(4) (LTD(4)) antagonistic activity of compounds having the basic quinolinyl(bridged)aryl framework. An approach combining pharmacophore mapping, molecule alignment, and CoMFA models was used to derive a hypothesis for a series of LTD(4) antagonists having the basic diaryl-bridged framework. In this compound series, the produced pharmacophore hypotheses have shown to yield molecule alignments suitable to derive valuable CoMFA models. Model selection focused on (1) obtention of coherent modeling results, (2) consistency with the available SAR data, and (3) ability to predict the activity of an independent set of congeneric molecules. This approach resulted in a combined pharmacophore and CoMFA model that can generally represent the antagonistic activity within a log unit of the measured value for compounds of the series. The resulting pharmacophore (model C) consists of an acidic or negative ionizable function (AC), a hydrogen-bond acceptor (HBA), and three hydrophobic regions (HY) and produces chemically meaningful alignments with the most active compounds of the series mapping the pharmacophore in a extended energetically favorable conformation.     

Three-dimensional common-feature hypotheses for Toll-like receptor 7 agonists

Toll-like receptor 7 (TLR7), the best known TLRs, has been demonstrated to be useful in fighting against infectious disease. In our study, three-dimensional (3D) pharmacophore models were constructed from a set of 5 TLR7 agonists. Among the 10 common-featured models generated by program Discovery Studio/HipHop, a hypothesis (Hypo2) including one hydrogen-bond donor (D), one hydrogen-bond acceptor (A), and two hydrophobic (H) features was considered to be important in evaluating the ligands with TLR7 agonistic activity. The obtained pharmacophore model was further validated using a set of test molecules and the Catalyst TLR7-agonist-subset database. Hypo2 has been shown to identify a range of highly potent TLR7 agonists. Finally, the obtained pharmacophore was further validated using docking studies. Taken together, this model can be utilized as a guide for future studies to design the structurally novel TLR7 agonists.     

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.     

A combined approach based on 3D pharmacophore and docking for identification of new aurora A kinase inhibitors

Aurora kinase A is involved in multiple mitotic events in cell cycle and has been identified as a major regulator of centrosome function in mitosis. Aurora A has been found to be over-expressed in many tumor types including breast, lung, colon, ovarian, pancreatic and glial cells. Thus, inhibition of aurora A can be a potential target in oncology. A five-point pharmacophore was generated using PHASE for a set of aurora A inhibitors reported in literature. The generated pharmacophore yielded statistically significant 3D-QSAR model, with a correlation coefficient r ² of 0.936 and q ² of 0.703. The pharmacophore indicated that presence of two aromatic ring features (R), two acceptor features (A) and one donor feature (D) is necessary for potent inhibitory activity. The database screening was done initially by use of pharmacophore followed by an interaction-based selection using docking. Twelve hits with satisfactory pharmacokinetic properties have been identified.     

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.     

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.     

Chemical function based pharmacophore generation of endothelin-A selective receptor antagonists

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.     

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。     

Discovery of novel DPP IV inhibitors: application of pharmacophore-based virtual screening

Dipeptidyl peptidase IV (DPP-IV) is a potential drug target for type-2 diabetes and DPP-IV inhibitors are known to efficiently improve glucose tolerance. In the present study, pharmacophore model for a set of 29 DPP-IV inhibitors was generated by ligand-based pharmacophore generation process. The best hypothesis, hypo 1, consisting of four chemical features, namely, one hydrogen bond donor, one hydrogen bond acceptor, one hydrophobe and one ring aromatic was validated by cost function analysis, test set prediction and Fischer test. The validated pharmacophore model was then used for searching new lead compounds from Maybridge and NCI database. Four compounds (CD01797, CD06202, CD02493, and AW01077) from Maybridge database and three compounds (NSC997, NSC2450, and NSC5815) from NCI database were identified as structurally diverse druggable novel leads with nM activity against DPP-IV.     

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase.

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.