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.     

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.     

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.     

3D-QSAR studies of some tetrasubstituted pyrazoles as COX-II inhibitors

Pharmacophore mapping studies were undertaken for a series of molecules belonging to tetrasubstituted pyrazoles as canine COX-II inhibitors. A six point pharmacophore with 3 hydrogen bond acceptors (A), one hydrophobic group (H) and two aromatic rings (R) as pharmacophoric feature was developed. The pharmacophoric hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.958. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.852 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore model describe the key structure-activity relationship of COX-II inhibitors, can predict their activities, and can thus be used to design novel inhibitors.     

Pharmacophore-based in silico high-throughput screening to identify novel topoisomerase-I inhibitors

Topoisomerase-I (TOP-I) has emerged as a potential target for the design and development of anticancer compounds. TOP-I inhibitors have shown promise in the treatment of various cancers including renal cell cancer, whose exact cause is yet to be known. Recent studies indicate that indenoisoquinolines can provide greater stability to drug-topoisomerase-DNA cleavage complexes, which makes them a more appropriate anticancer class of compounds compared to camptothecin. In view of such significance, a three-dimensional pharmacophore model has been developed using a training set of 36 indenoisoquinoline-based topoisomerase inhibitors. The validated best model consists of three chemical features: one hydrophobic, one positive ionizable, and one ring aromatic with good correlation values of r _(training) ²  = 0.827 and r _(test) ²  = 0.702. Furthermore, 98 % validation by CatScramble method and a good r ² of 0.703 from 22 external test set compounds have testified the universal applicability of the generated model. Validated three feature pharmacophore model has been used to screen the chemical database from the National Cancer Institute (NCI) leading to the identification of 17 druggable TOP-I inhibitors which can be raised into drug candidates after further evaluation.     

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

Aim:

Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches.

Methods:

The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations.

Results:

The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer''s randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10.

Conclusion:

Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.     

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.     

A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on it

In this study, 3D‐pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross‐validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

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.     

Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification

Chemical feature based pharmacophore models were elaborated for angiotensin II receptor subtype 1 (AT(1)) antagonists using both a quantitative and a qualitative approach (Catalyst HypoGen and HipHop algorithms, respectively). The training sets for quantitative model generation consisted of 25 selective AT(1) antagonists exhibiting IC(50) values ranging from 1.3 nM to 150 microM. Additionally, a qualitative pharmacophore hypothesis was derived from multiconformational structure models of the two highly active AT(1) antagonists 4u (IC(50) = 0.2 nM) and 3k (IC(50) = 0.7 nM). In the case of the quantitative model, the best pharmacophore hypothesis consisted of a five-features model (Hypo1: seven points, one hydrophobic aromatic, one hydrophobic aliphatic, a hydrogen bond acceptor, a negative ionizable function, and an aromatic plane function). The best qualitative model consisted of seven features (Hypo2: 11 points, two aromatic rings, two hydrogen bond acceptors, a negative ionizable function, and two hydrophobic functions). The obtained pharmacophore models were validated on a wide set of test molecules. They were shown to be able to identify a range of highly potent AT(1) antagonists, among those a number of recently launched drugs and some candidates presently undergoing clinical tests and/or development phases. The results of our study provide confidence for the utility of the selected chemical feature based pharmacophore models to retrieve structurally diverse compounds with desired biological activity by virtual screening.     

Ligand-based drug design studies using predictive pharmacophore model generation on 4H-1,2,4-triazoles as AT1 receptor antagonists

Angiotensin II subtype I receptor constitutes a successful target for the treatment of hypertension and cardiovascular diseases. In this study, the pharmacophore search was used to identify structural features that are common in the set of 4H-1,2,4-triazoles. The HypoGen algorithms implemented in the Catalyst software package was employed to create quantitative models. The common feature model for AT₁ included two ring aromatic features and two hydrophobic features with statistical values of 0.88 as correlation coefficient for training set. The best model Hypo 1 was validated using a test set of 38 compounds and cat-scramble validation method.     

In silico ligand-based pharmacophore model generation for the identification of novel Pneumocystis carinii DHFR inhibitors

The ubiquitous enzyme, dihydrofolate reductase (DHFR), is a well-studied metabolic enzyme of significant pharmacological relevance. The past research studies have shown that DHFR has emerged as an important therapeutic target for the treatment of various diseases including cancers, bacterial and protozoal infections, and the opportunistic infections that are associated with AIDS. DHFR has been successfully used and selected as a target for the purpose of devising a cure for various diseases by discovering the antimicrobial drugs against a range of pathogenic microorganisms including the opportunistic microorganisms Pneumocystis carinii (pc), Toxoplasma gondii and Mycobacterium avium complex. However, it has been reported that blockage of the enzymatic activity of DHFR is a crucial and most significant element in terms of the treatment of a wide range of diseases. DHFR is also a key enzyme in the treatment of Pneumocystosis. The discovery and development of type-specific pcDHFR inhibitors is of both research and clinical interests. Ligand-based pharmacophore modeling is playing a vital role for the identification of ligand features for the particular targets. In this study, we present a model for the design of ligand-based pharmacophore onto the set of 10 compounds of eight different classes along with the standard drug trimethoprim. The ligand-based pharmacophore model has been identified to facilitate the discovery of type-specific pcDHFR inhibitors. A pharmacophore model was generated using Ligand Scout 3.02 with diverse classes of pcDHFR inhibitors. The proposed pharmacophore model generated in this study revealed that the model contains two HBAs, two HBDs, and one HY/AR volume. Ligand Scout 3.02 has been used to predict the pharmacophore features for pcDHFR inhibitors, and the distances between pharmacophore features have been computed by the effective use of the software VMD. The results indicate that the in silico methods are valuable for the prediction of the biological activity of the compound or compound library by screening it against a predicted pharmacophore. Thus, the results obtained in this study can be considered to be useful and reliable tools for the identification of structurally diverse compounds with the desired biological activity. The model has also been validated, firstly by mapping of five test compounds on to our model and secondly by docking these test compounds into the active site of pcDHFR.     

苯甲酰脲类抗肿瘤β微管蛋白抑制剂药效团模型的构建与应用

采用Catalyst软件包, 选择抗急性淋巴白血病细胞系活性相差较大的2种结构类型的17个苯甲酰脲类β微管蛋白抑制剂化合物作为训练集, 经构象分析, 构建出最佳药效团模型, 该模型含有2个疏水中心 (HP) 和2个氢键受体 (HBA), 具有良好的活性预测能力 (RMS = 0.43, Correl = 0.98, Weight = 2.06, Config = 15.97), 有利于设计和改造具有新型结构的β微管蛋白抑制剂。     

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.     

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.     

Discovery of novel mesangial cell proliferation inhibitors using a three-dimensional database searching method.

A three-dimensional pharmacophore model of mesangial cell (MC) proliferation inhibitors was generated from a training set of 4-(diethoxyphosphoryl)methyl-N-(3-phenyl-[1,2,4]thiadiazol-5-yl)benzamide, 2, and its derivatives using the Catalyst/HIPHOP software program. On the basis of the in vitro MC proliferation inhibitory activity, a pharmacophore model was generated as seven features consisting of two hydrophobic regions, two hydrophobic aromatic regions, and three hydrogen bond acceptors. Using this model as a three-dimensional query to search the Maybridge database, structurally novel 41 compounds were identified. The evaluation of MC proliferation inhibitory activity using available samples from the 41 identified compounds exhibited over 50% inhibitory activity at the 100 nM range. Interestingly, the newly identified compounds by the 3D database searching method exhibited the reduced inhibition of normal proximal tubular epithelial cell proliferation compared to a training set of compounds.     

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.     

The discovery of potential cyclin A/CDK2 inhibitors: a combination of 3D QSAR pharmacophore modeling, virtual screening, and molecular docking studies

Cyclin-dependent kinases are a family of enzymes that regulates the cell cycle process. They have been found to be novel targets for potential anti-cancer drugs. In the present study, a 3D pharmacophore model has been developed for cyclin A/CDK2 from its known inhibitors. The most reliable quantitative HypoGen model (Hypo1) consists of two hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic feature. Hypo1, with a correlation coefficient of 0.98, a root mean square deviation of 0.84, a configuration cost of 16.25 and a cost difference of 102.93, showed a remarkable predictive power and has >90 % probability of representing a true correlation in the activity data. The model was validated using Fisher’s test at 95 % confidence level and test set prediction (r = 0.96). Hypo1 was then employed for virtual screening of Life Chemicals and NCI2003 databases of which multiple conformations were generated for each compound (596,030 compounds, 45,603,414 conformers). Hits were filtered according to the Lipinski, Ghose, and Veber’s rules. Following docking simulations, consensus scoring was used to determine the ligand poses that interact best with the protein binding site and to reduce number of false positives. 11 hits were ultimately selected as potent candidate leads. This work may help in the identification or design of novel anti-cancer drugs based on hits determined. The pharmacophore model obtained and validated in this study can be used as a three-dimensional query in searches for CDK2 inhibitors in additional compound databases.