Pharmacophore identification of Raf-1 kinase inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available Raf-1 kinase inhibitors. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic groups), has a correlation coefficient of 0.972. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.     

Pharmacophore models generation by catalyst and phase consensus-based virtual screening protocol against PI3Kα inhibitors

Phosphoinositide 3-kinases (PI3Ks) family has emerged as promising targets for novel therapeutic agents against neoplastic diseases. Pharmacophore and 3D-quantitative structure–activity relationship modelling were applied to study the structure–activity relationship of PI3K inhibitors. The best HypoGen pharmacophore hypothesis Hypo1 with a correlation coefficient of 0.961 consists of one hydrogen-bond acceptor, one hydrogen-bond donor and two hydrophobic features, whereas the best phase hypothesis AADRRR.378 with favourable statistics (q² = 0.7368, r² = 0.9863) has two hydrogen-bond acceptors, one hydrogen-bond donor and three ring aromatic features. Multiple methods, such as Fischer validation, molecular docking and mapping of test set molecules, were carried out to validate these pharmacophore models. Furthermore, a comparative molecular similarity indices analysis candidate hypothesis model was generated as a supplement of pharmacophore hypothesis. Detailed protein–ligand binding information obtained by Glide was utilised in compound optimisation and virtual screening. A molecular database of 133 known inhibitors and 6179 decoys was built for a screening test to quantitatively analyse various hypotheses and scoring parameters. Finally, we designed a workflow integrating HypoGen pharmacophore searching, phase pharmacophore searching and molecular docking for screening the database. With an improved criterion of enrichment factor (EF = 17.43) and ROC curve (AUC = 0.946), this workflow would provide us an original method for novel PI3K inhibitors.     

Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors

Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer’s randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski’s rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation.     

Pharmacophore identification of alpha(1A)-adrenoceptor antagonists

A chemical feature based pharmacophore model was developed for alpha(1A)-adrenoceptor antagonists by HypoGen module implemented in catalyst software package. The best scoring pharmacophore hypothesis, Hypo1, consisted of four important chemical features (one positive ion, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group). The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.     

Chemical function-based pharmacophore generation of selective κ-opioid receptor agonists by catalyst and phase

Two chemical function-based pharmacophore models of selective κ-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent κ-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel κ-agonists.     

Integrated ligand and structure based studies of flavonoids as fatty acid biosynthesis inhibitors of Plasmodium falciparum

A common feature pharmacophore with two hydrogen-bond acceptor and one aromatic hydrophobic feature has been generated using seven active flavonoids. Docking studies of these compounds well corroborates with the pharmacophore model. Therefore models could be useful for identification of potential novel FAS-II inhibitors.     

Receptor-guided 3D-QSAR approach for the discovery of c-kit tyrosine kinase inhibitors

Studies of the the three-dimensional quantitative structure-activity relationships for ninety-five c-kit tyrosine kinase inhibitors were performed. Based on a co-crystallized compound (1 T46), known inhibitors were aligned with c-kit by induced-fit docking, and multiple training/test set splitting was performed to validate the selected pharmacophore model. The best pharmacophore model consisted of five features: one hydrogen-bond donor and four aromatic rings. Reliable statistics were obtained (R(2) = 0.95, R(pred) (2) = 0.75), and the model was validated by using it to select c-kit inhibitors from a database; 82.1% of the hits it retrieved were active. Accordingly, our model can be reliably used to identify new c-kit inhibitors, and can provide useful information when designing new inhibitors.     

Neuraminidase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 22 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity, for discovering new potent neuraminidase (NA) inhibitors to fight against avian influenza virus. The best hypothesis (Hypo1), consisting of five features, namely, one positive ionizable group, one negative ionizable group, one hydrophobic point, and two hydrogen-bond donors, has a correlation coefficient of 0.902, a root mean square deviation of 1.392, and a cost difference of 72.88, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 88 known NA inhibitors in our test set with a correlation coefficient of 0.818 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.     

Farnesyltransferase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity as required by HypoGen program in the Catalyst software, for discovering new farnesyltransferase (FTase) inhibitors. The best hypothesis (Hypo1), consisting of four features, namely, two hydrogen-bond acceptors, one hydrophobic point, and one ring aromatic feature, has a correlation coefficient of 0.949, a root-mean-square deviation of 1.321, and a cost difference of 163.15, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 227 known FTase inhibitors in our test set with a correlation coefficient of 0.776 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.     

Three-dimensional pharmacophore hypotheses of octopamine/tyramine agonists which inhibit [1-14C]acetate incorporation in Plodia interpunctella

Three-dimensional pharmacophore hypotheses were built from a set of 36 octopamine (OA)/tyramine (TA) agonists responsible for the inhibition of sex-pheromone production in Plodia interpunctella. Among the ten chemical-featured models generated by a program Catalyst/Hypo, hypotheses including hydrogen-bond acceptor (HBA), hydrogen-bond acceptor aliphatic (HBAl), hydrophobic (Hp), hydrophobic aromatic (HpAr) and hydrophobic aliphatic (HpAl) features were considered to be important and predictive in evaluating OA/TA agonists. Active agonists mapped well onto all the features of the hypothesis such as HBA, HBAl, Hp, HpAr and HpAl features. On the other hand, inactive compounds were shown to be poorly capable of achieving an energetically favorable conformation shared by the active molecules in order to fit the 3-D chemical-feature pharmacophore models. Those hypotheses are considered to be used in designing new leads for hopefully more active compounds. Further research on the comparison of models from the agonists may help elucidate the mechanisms of OA/TA receptor-ligand interactions.     

Identification of novel, less toxic PTP-LAR inhibitors using in silico strategies: pharmacophore modeling, SADMET-based virtual screening and docking

Human leukocyte antigen-related (PTP-LAR) is a receptor-like transmembrane phosphatase and a potential target for diabetes, obesity and cancer. In the present study, a sequence of in silico strategies (pharmacophore mapping, a 3D database searching, SADMET screening, and docking and toxicity studies) was performed to identify eight novel nontoxic PTP-LAR inhibitors. Twenty different pharmacophore hypotheses were generated using two methods; the best (hypothesis 2) consisted of three hydrogen-bond acceptor (A), one ring aromatic (R), and one hydrophobic aliphatic (Z) features. This hypothesis was used to screen molecules from several databases, such as Specs, IBS, MiniMaybridge, NCI, and an in-house PTP inhibitor database. In order to overcome the general bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski’s rule of five and SADMET properties and validated by molecular docking studies using the available crystal structure 1LAR. These docking studies suggested the ligand binding pattern and interactions required for LAR inhibition. The docking analysis also revealed that sulfonylurea derivatives with an isoquinoline or naphthalene scaffold represent potential LAR drugs. The screening protocol was further validated using ligand pharmacophore mapping studies, which showed that the abovementioned interactions are indeed crucial and that the screened molecules can be presumed to possess potent inhibitory activities.     

Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches

In our study, a structure-based virtual screening study was conducted to identify potent ITK inhibitors, as ITK is considered to play an important role in the treatment of inflammatory diseases. We developed a structure-based pharmacophore model using the crystal structure (PDB ID: 3MJ2) of ITK complexed with BMS-50944. The most predictive model, SB-Hypo1, consisted of six features: three hydrogen-bond acceptors (HBA), one hydrogen-bond donor (HBD), one ring aromatic (RA), and one hydrophobic (HY). The statistical significance of SB-Hypo1 was validated using wide range of test set molecules and a decoy set. The resulting well-validated model could then be confidently used as a 3D query to screen for drug-like molecules in a database, in order to retrieve new chemical scaffolds that may be potent ITK inhibitors. The hits retrieved from this search were filtered based on the maximum fit value, drug-likeness, and ADMET properties, and the hits that were retained were used in a molecular docking study to find the binding mode and molecular interactions with crucial residues at the active site of the protein. These hits were then fed into a molecular dynamics simulation to study the flexibility of the activation loop of ITK upon ligand binding. This combination of methodologies is a valuable tool for identifying structurally diverse molecules with desired biological activities, and for designing new classes of selective ITK inhibitors.

Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors

Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. 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. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.     

Identification of dual kinase inhibitors of CK2 and GSK3β: combined qualitative and quantitative pharmacophore modeling approach

PTEN, a tumor suppressor protein, gets deactivated by casein kinase 2 (CK2) and glycogen synthase kinase 3β (GSK3β), which are the major causes of PI3K/AKT-driven tumors. To surmount this problem, the multi-target inhibitor strategy may be of great significance. The goal of this study was to design dual-target inhibitors of CK2 and GSK3β using a combination of pharmacophore modeling and molecular docking studies. The common feature-based (qualitative) and 3DQSAR-based (quantitative) pharmacophore models were generated and validated. The best pharmacophore models (Pharm18 and Hypo1) comprised two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic features. The models were used to screen various chemical database and top mapped compounds from each database were selected. They were processed for Lipinski filter, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, and docking studies. We have obtained six hits with comparable dock score to the reported inhibitors. We have concluded Hit15 as a competent candidate based on its docking and Density Functional Theory (DFT) calculations. It showed 140.73 and 130.79 dock score in CK2 and GSK3β, respectively. The electronic property of Hit 15 showed the lowest energy gap (0.021) compared to other hits and active ligands which suggest its higher reactivity. In conclusion, this study may assist in the development of new potent dual kinase inhibitors of CK2 and GSK3β. Also, the overture effort of combined qualitative and quantitative modeling for the development of multi-target inhibitors may support the future endeavors.     

Three-dimensional pharmacophore hypotheses for the locust neuronal octopamine receptor (OAR3). Part 2: agonists.

Three-dimensional pharmacophore hypotheses were built from a set of 43 agonists against octopamine receptor class 3 (OAR3) in locust nervous tissue. Among the 10 chemical-featured models generated by program Catalyst/Hypo, a hypothesis including hydrogen-bond acceptor (HBA), hydrophobic (Hp), and hydrophobic aliphatic (HpA1) features was considered to be important and predictive in evaluating OAR3 agonists. While the ideal and null hypotheses had a cost of 156.40 and 239.20, respectively, the 10 resulting hypotheses possessed costs from 169.89 to 175.81. The best hypothesis that was confirmed to have a 95% chance of true correlation yielded a low RMS of 0.757 and high regression r of 0.933. Active agonists mapped well onto all the features of the hypothesis such as HBA, Hp, and HpA1. On the other hand, inactive compounds were shown to be difficult to achieve the energetically favorable conformation which is found in the active molecules in order to fit the 3-D chemical feature pharmacophore models.     

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

Mesenchymal epithelial transition factor (c-Met) is an attractive target for cancer therapy. Three-dimensional pharmacophore hypotheses were built based on a set of known structurally diverse c-Met inhibitors. The best pharmacophore model, which identified inhibitors with an associated correlation coefficient of 0.983 between their experimental and estimated IC(50) values, consisted of two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic feature. The highly predictive power of the model was rigorously validated by test set prediction and Fischer's randomization method. The high values of enrichment factor and receiver operating characteristic (ROC) score indicated the model performed fairly well at distinguishing active from inactive compounds. The model was then applied to screen compound database for potential c-Met inhibitors. A filtering protocol, including druggability and molecular docking, were also applied in hits selection. The final 38 molecules, which exhibited good estimated activities, desired binding mode and favorable drug likeness were identified as potential c-Met inhibitors. Their novel backbone structures could be served as scaffolds for further study, which may facilitate the discovery and rational design of potent c-Met kinase inhibitors.     

Pharmacophore modeling and virtual screening for designing potential 5-Lipoxygenase inhibitors

Inhibitors of the 5-Lipoxygenase (5-LOX) pathway have a therapeutic potential in a variety of inflammatory disorders such as asthma. In this study, chemical feature based pharmacophore models of inhibitors of 5-LOX have been developed with the aid of HipHop and HypoGen modules within Catalyst program package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.97), consists of two hydrogen-bond acceptors, one hydrophobic feature and one ring aromatic feature. Hypo1 was further validated by test set and cross validation method. The application of the model shows great success in predicting the activities of 65 known 5-LOX inhibitors in our test set with a correlation coefficient of 0.85 with a cross validation of 95% confidence level, proving that the model is reliable in identifying structurally diverse compounds for inhibitory activity against 5-LOX. Furthermore, Hypo1 was used as a 3D query for screening Maybridge and NCI databases within catalyst and also drug like compounds obtained from Enamine Ltd, which follow Lipinski’s rule of five. The hit compounds were subsequently subjected to filtering by docking and visualization, to identify the potential lead molecules. Finally 5 potential lead compounds, identified in the above process, were evaluated for their inhibitory activities. These studies resulted in the identification of two compounds with potent inhibition of 5-LOX activity with IC₅₀ of 14 μM and 35 μM, respectively. These studies thus validate the pharmacophore model generated and suggest the usefulness of the model in screening of various small molecule libraries and identification of potential lead compounds for 5-LOX inhibition.     

Constrained NBMPR analogue synthesis, pharmacophore mapping and 3D-QSAR modeling of equilibrative nucleoside transporter 1 (ENT1) inhibitory activity

Conformationally constrained analogue synthesis was undertaken to aid in pharmacophore mapping and 3D-QSAR analysis of nitrobenzylmercaptopurine riboside (NBMPR) congeners as equilibriative nucleoside transporter 1 (ENT1) inhibitors. In our previous study [J. Med. Chem. 2003, 46, 831-837], novel regioisomeric nitro-1,2,3,4-tetrahydroisoquinoline conformationally constrained analogues of NBMPR were synthesized and evaluated as ENT1 ligands. 7-NO(2)-1,2,3,4-Tetrahydroisoquino-2-yl purine riboside was identified as the analogue with the nitro group in the best orientation at the NBMPR binding site of ENT1. In the present study, further conformational constraining was introduced by synthesizing 5'-O,8-cyclo derivatives. The flow cytometrically determined binding affinities indicated that the additional 5'-O,8-cyclo constraining was unfavorable for binding to the ENT1 transporter. The structure-activity relationship (SAR) acquired was applied to pharmacophore mapping using the PHASE program. The best pharmacophore hypothesis obtained embodied an anti-conformation with three hydrogen-bond acceptors, one hydrophobic center, and two aromatic rings involving the 3'-OH, 4'-oxygen, the NO(2) group, the benzyl phenyl and the imidazole and pyrimidine portions of the purine ring, respectively. A PHASE 3D-QSAR model derived with this pharmacophore yielded an r(2) of 0.916 for four (4) PLS components, and an excellent external test set predictive r(2) of 0.78 for 39 compounds. This pharmacophore was used for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study that also afforded a predictive model with external test set validation predictive r(2) of 0.73. Thus, although limited, this study suggests that the bioactive conformation for NBMPR at the ENT1 transporter could be anti. The study has also suggested an ENT1 inhibitory pharmacophore, and established a predictive CoMFA 3D-QSAR model that might be useful for novel ENT1 inhibitor discovery and optimization.     

Discovery of novel 5α-reductase type II inhibitors by pharmacophore modelling,virtual screening,molecular docking and molecular dynamics simulations

Benign prostatic hyperplasia (BPH) is caused by augmented levels of androgen dihydrotestosterone (DHT) which is involved in the growth of the prostate in humans. 5α-Reductase type II (5αR2) is an intracellular enzyme that catalyses the formation of DHT from testosterone; hence, the inhibition of 5αR2 has emerged as one of the most promising strategies for the treatment of BPH. In this study, a computational approach that integrates ligand-based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics (MD) simulations was adopted to discover novel 5αR2 inhibitors with less side effects. After validating by Fischer's randomisation and Güner–Henry test, the best quantitative pharmacophore model (Hypo1), consisting of two hydrogen-bond acceptors and three hydrophobic features, was subsequently used as a three-dimensional-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute databases. These hits were further filtered by ADMET (absorption, distribution, metabolism, elimination and toxicology) and molecular docking experiments, and their binding stabilities were validated by 10-ns MD simulations. Finally, only one hit was identified as a potential lead based on higher predicted inhibitory activity to 5αR2 compared with the most active inhibitor (finasteride). Our results further suggest that this potential lead could easily be synthesised and has structural novelty, making it a promising candidate for treating BPH.