Identification of Novel HIV 1- Protease Inhibitors: Application of Ligand and Structure Based Pharmacophore Mapping and Virtual Screening

A combined ligand and structure-based drug design approach provides a synergistic advantage over either methods performed individually. Present work bestows a good assembly of ligand and structure-based pharmacophore generation concept. Ligand-oriented study was accomplished by employing the HypoGen module of Catalyst in which we have translated the experimental findings into 3-D pharmacophore models by identifying key features (four point pharmacophore) necessary for interaction of the inhibitors with the active site of HIV-1 protease enzyme using a training set of 33 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond acceptors and two hydrophobic, showed a correlation (r) of 0.90 and a root mean square of 0.71 and cost difference of 56.59 bits between null cost and fixed cost. The model was validated using CatScramble technique, internal and external test set prediction. In the second phase of our study, a structure-based five feature pharmacophore hypothesis was generated which signifies the importance of hydrogen bond donor, hydrogen bond acceptors and hydrophobic interaction between the HIV-1 protease enzyme and its inhibitors. This work has taken a significant step towards the full integration of ligand and structure-based drug design methodologies as pharmacophoric features retrieved from structure-based strategy complemented the features from ligand-based study hence proving the accuracy of the developed models. The ligand-based pharmacophore model was used in virtual screening of Maybridge and NCI compound database resulting in the identification of four structurally diverse druggable compounds with nM activities.     

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149) and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM) and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.     

Evaluation of the combination mode and features of p38 MAPK inhibitors: construction of different pharmacophore models and molecular docking

Abnormal expression of tumour necrosis factor-α (TNF-α) can lead to various pathological reactions, such as arthritis, psoriasis, krone disease, etc. p38 mitogen-activated protein kinase (p38 MAPK) is an important signal transduction enzyme that plays important roles in influencing the release of intracellular TNF-α factor. It is very meaningful to study the targeting kinase with specific inhibitors in the treatment of related diseases. In order to achieve a deeper insight, it is necessary to analyse the structural characteristics and the action mode of the p38 MAPK inhibitors in the active site. In the study, a ligand-based common feature pharmacophore model and the receptor structure-based pharmacophore model were constructed, respectively. Their common chemical features consisted of the hydrophobic groups (H) and the hydrogen bond acceptors (A), and kept the consistency of spatial structure distribution. Then, the molecular docking and molecular dynamics simulation were performed with the eight training set compounds. The binding characteristics of molecules binding were described in the topological region of the active site. Finally, the structure–activity relationship (SAR) was obtained by analysing docking results with the different pharmacophore models. This research leads to the proposal of an interaction model in the p38 MAPK active site and provides guidance for the screening and design of more potent and selective p38 MAPK inhibitors.     

Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3)

The clinical efficacy of multiple kinase inhibitors has caught the interest of Pharmaceutical and Biotech researchers to develop potential drugs with multi-kinase inhibitory activity for complex diseases. In the present work, we attempted to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3), keys players in immune signaling, by developing ideal pharmacophores integrating Ligand-based pharmacophore models (LBPMs) and Structure-based pharmacophore models (SBPMs), thereby projecting the optimum pharmacophoric required for inhibition of both the kinases. The four point LBPM; ADPR.14 suggested the presence of one hydrogen bond acceptor, one hydrogen bond donor, one positive ionizable, and one ring aromatic feature for Syk inhibitory activity and AADH.54 proposed the necessity of two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature for JAK3 inhibitory activity. To our interest, SBPMs identified additional ring aromatic features required for inhibition of both the kinases. For Syk inhibitory activity, the hydrogen bond acceptor feature indicated by LBPM was devoid of forming hydrogen bonding interaction with the hinge region amino acid residue (Ala451). Thus merging the information revealed by both LBPMs and SBPMs, ideal pharmacophore models i.e. ADPRR.14 (Syk) and AADHR.54 (JAK3) were generated. These models after rigorous statistical validation were used for screening of Asinex database. The systematic virtual screening protocol, including pharmacophore and docking-based screening, ADME property, and MM-GBSA energy calculations, retrieved final 10 hits as dual inhibitors of Syk and JAK3. Final 10 hits thus obtained can aid in the development of potential therapeutic agents for autoimmune disorders. Also the top two hits were evaluated against both the enzymes.     

Molecular docking and pharmacophore model studies of Rho kinase inhibitors

Molecular docking and pharmacophore model approaches were used to characterise the binding features of four different series of Rho kinase (ROCK) inhibitors. Docking simulation of 20 inhibitors with ROCK was performed. The binding conformations and binding affinities of these inhibitors were obtained using AutoDock 4.0 software. The predicted binding affinities correlate well with the activities of these inhibitors (R ² = 0.904). 3D pharmacophore models were generated for ROCK based on highly active inhibitors implemented in Catalyst 4.11 program. The best pharmacophore model consists of one hydrogen bond acceptor feature and two hydrophobic features, and they all seemed to be essential for inhibitors in terms of their binding activities. It is anticipated that the findings reported in this paper may provide very useful information for designing new ROCK inhibitors.     

Exploration of Novel Inhibitors for Bruton’s Tyrosine Kinase by 3D QSAR Modeling and Molecular Dynamics Simulation

Bruton’s tyrosine kinase (BTK) is a cytoplasmic, non-receptor tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in many cellular signaling pathways. B cell malignancies are dependent on BCR signaling, thus making BTK an efficient therapeutic target. Over the last few years, significant efforts have been made in order to develop BTK inhibitors to treat B-cell malignancies, and autoimmunity or allergy/hypersensitivity but limited success has been achieved. Here in this study, 3D QSAR pharmacophore models were generated for Btk based on known IC₅₀ values and experimental energy scores with extensive validations. The five features pharmacophore model, Hypo1, includes one hydrogen bond acceptor lipid, one hydrogen bond donor, and three hydrophobic features, which has the highest correlation coefficient (0.98), cost difference (112.87), and low RMS (1.68). It was further validated by the Fisher’s randomization method and test set. The well validated Hypo1 was used as a 3D query to search novel Btk inhibitors with different chemical scaffold using high throughput virtual screening technique. The screened compounds were further sorted by applying ADMET properties, Lipinski’s rule of five and molecular docking studies to refine the retrieved hits. Furthermore, molecular dynamic simulation was employed to study the stability of docked conformation and to investigate the binding interactions in detail. Several important hydrogen bonds with Btk were revealed, which includes the gatekeeper residues Glu475 and Met 477 at the hinge region. Overall, this study suggests that the proposed hits may be more effective inhibitors for cancer and autoimmune therapy.     

Substrate specificity of the ileal and the hepatic Na(+)/bile acid cotransporters of the rabbit. II. A reliable 3D QSAR pharmacophore model for the ileal Na(+)/bile acid cotransporter

To design a reliable 3D QSAR model of the intestinal Na(+)/bile acid cotransporter, we have used a training set of 17 inhibitors of the rabbit ileal Na(+)/bile acid cotransporter. The IC(50) values of the training set of compounds covered a range of four orders of magnitude for inhibition of [(3)H]cholyltaurine uptake by CHO cells expressing the rabbit ileal Na(+)/bile acid cotransporter allowing the generation of a pharmacophore using the CATALYST algorithm. After thorough conformational analysis of each molecule, CATALYST generated a pharmacophore model characterized by five chemical features: one hydrogen bond donor, one hydrogen bond acceptor, and three hydrophobic features. The 3D pharmacophore was enantiospecific and correctly estimated the activities of the members of the training set. The predicted interactions of natural bile acids with the pharmacophore model of the ileal Na(+)/bile acid cotransporter explain exactly the experimentally found structure;-activity relationships for the interaction of bile acids with the ileal Na(+)/bile acid cotransporter (Kramer et al. 1999. J. Lipid. Res. 40: 1604;-1617). The natural bile acid analogues cholyltaurine, chenodeoxycholyltaurine, or deoxycholyltaurine were able to map four of the five features of the pharmacophore model: a) the five-membered ring D and the methyl group at position 18 map one hydrophobic site and the 21-methyl group of the side chain maps a second hydrophobic site; b) one of the alpha-oriented hydroxyl groups at position 7 or 12 fits the hydrogen bond donor feature; c) the negatively charged side chain acts as hydrogen bond acceptor; and d) the hydroxy group at position 3 does not specifically map any of the five binding features of the pharmacophore model. The 3-hydroxy group of natural bile acids is not essential for interactions with ileal or hepatic Na(+)/bile acid cotransporters. A modification of the 3-position of a natural bile acid molecule is therefore the preferred position for drug targeting strategies using bile acid transport pathways.     

Discovery of novel human phenylethanolamine N-methyltransferase (hPNMT) inhibitors using 3D pharmacophore-Based in silico, biophysical screening and enzymatic activity assays

With the aid of receptor-oriented pharmacophore-based in silico screening, we established three pharmacophore maps explaining the binding model of hPNMT and a known inhibitor, SK&F 29661 (Martin et al., 2001). The compound library was searched using these maps. Nineteen selected candidate inhibitors of hPNMT were screened using STD-NMR and fluorescence experiments. An enzymatic activity assay based on HPLC was additionally performed. Consequently, three potential hPNMT inhibitors were identified, specifically, 4-oxo-1,4-dihydroquinoline-3,7-dicarboxylic acid, 4-(benzo[d][1,3]dioxol-5-ylamino)-4-oxobutanoic acid, and 1,4-diaminonaphthalene-2,6-disulfonic acid. These novel inhibitors were retrieved using Map II comprising one hydrogen bond acceptor, one hydrogen bond donor, one lipophilic feature, and shape constraints, including a hydrogen bond between Lys57 of hPNMT and a hydrogen bond donor of the inhibitor, and stacked hydrophobic interactions between the side-chain of Phe182 and an aromatic region of the inhibitor. Water-mediated interactions between Asn267 and Asn39 of hPNMT and the amide or amine group of three potent inhibitors were additional important features for hPNMT activity. The binding model presented here may be applied to identify inhibitors with higher potency. Moreover, our novel compounds are valuable candidates for further lead optimization of PNMT inhibitors.     

Pharmacoinformatics analysis to identify inhibitors of Mtb-ASADH

Aspartate-semialdehyde dehydrogenase (ASADH; EC 1.2.1.11) is a key enzyme in the biosynthesis of essential amino acids in prokaryotes and fungi, inhibition of ASADH leads to the development of novel antitubercular agents. In the present work, a combined structure and ligand-based pharmacophore modeling, molecular docking, and molecular dynamics (MD) approaches were employed to identify potent inhibitors of mycobacterium tuberculosis (Mtb)-ASADH. The structure-based pharmacophore hypothesis consists of three hydrogen bond acceptor (HBA), two negatively ionizable, and one positively ionizable center, while ligand-based pharmacophore consists of additional one HBA and one hydrogen bond donor features. The validated pharmacophore models were used to screen the chemical databases (ZINC and NCI). The screened hits were subjected to ADME and toxicity filters, and subsequently to the molecular docking analysis. Best-docked 25 compounds carry the characteristics of highly electronegative functional groups (–COOH and –NO₂) on both sides and exhibited the H-bonding interactions with highly conserved residues Arg99, Arg249, and His256. For further validation of docking results, MD simulation studies were carried out on two representative compounds NSC51108 and ZINC04203124. Both the compounds remain bound to the key active residues of Mtb-ASADH during the MD simulations. These identified hits can be further used for lead optimization and in the design more potent inhibitors against Mtb-ASADH.     

Pharmacophore mapping of a series of pyrrolopyrimidines,indolopyrimidines and their congeners as multidrug-resistance-associated protein (MRP1) modulators

Pharmacophore mapping studies were undertaken for a series of molecules belonging to pyrrolopyrimidines, indolopyrimidines and their congeners as multidrug resistance-associated protein (MRP1) modulators. A five-point pharmacophore with two hydrogen bond acceptors (A), one lipophilic/hydrophobic group (H), one positive ionic feature (P) and one aromatic ring (R) as pharmacophoric features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r ² = 0.799 for training set molecules. The model generated showed excellent predictive power, with a correlation coefficient Q ² = 0.679 for an external test set of 20 molecules. The pharmacophore was further validated using four structurally diverse compounds with MRP1 modulatory activity. These compounds mapped well onto four of the five features of the pharmacophore. The pharmacophore proposed here was then utilised for the successful retrieval of active molecules with diverse chemotypes from database search. The geometry and features of pharmacophore are expected to be useful for the design of selective MRP1 inhibitors. Figure Alignment of multidrug resistance-associated protein (MRP1) inhibitors with the developed pharmacophore. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r(2)?=?0.92, SD?=?0.16, F?=?84.8, N?=?40) and for test set (Q(2)?=?0.71, RMSE?=?0.06, Pearson R?=?0.90, N?=?10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.     

Discovery of cyclin-dependent kinase inhibitor, CR229, using structurebased drug screening

To generate new scaffold candidates as highly selective and potent cyclin-dependent kinase (CDK) inhibitors, structure-based drug screening was performed utilizing 3D pharmacophore conformations of known potent inhibitors. As a result, CR229 (6-bromo-2,3,4,9-tetrahydro-carbolin-1-one) was generated as the hit-compound. A computational docking study using the X-ray crystallographic structure of CDK2 in complex with CR229 was evaluated. This predicted binding mode study of CR229 with CDK2 demonstrated that CR229 interacted effectively with the Leu83 and Glu81 residues in the ATP-binding pocket of CDK2 for the possible hydrogen bond formation. Furthermore, biochemical studies on inhibitory effects of CR229 on various kinases in the human cervical cancer HeLa cells demonstrated that CR229 was a potent inhibitor of CDK2 (IC50: 3 microM), CDK1 (IC50: 4.9 microM), and CDK4 (IC50: 3 microM), yet had much less inhibitory effect (IC50: >20 microM) on other kinases, such as casein kinase 2-1 (CK2- alpha1), protein kinase A (PKA), and protein kinase C (PKC). Accordingly, these data demonstrate that CR229 is a potent CDK inhibitor with anticancer efficacy.     

Stepwise development of structure–activity relationship of diverse PARP-1 inhibitors through comparative and validated in silico modeling techniques and molecular dynamics simulation

Inhibitors of poly (ADP-ribose) polymerase-1 (PARP-1) enzyme are useful for the treatment of various diseases including cancer. Comparative in silico studies were performed on different ligand-based (2D-QSAR, Kernel-based partial least square (KPLS) analysis, Pharmacophore Search Engine (PHASE) pharmacophore mapping), and structure-based (molecular docking, MM-GBSA analyses, Gaussian-based 3D-QSAR analyses on docked poses) modeling techniques to explore the structure–activity relationship of a diverse set of PARP-1 inhibitors. Two-dimensional (2D)-QSAR highlighted the importance of charge topological index (JGI7), fractional polar surface area (Jurs_FPSA3), and connectivity index (CIC2) along with different molecular fragments. Favorable and unfavorable fingerprints were demonstrated in KPLS analysis, whereas important pharmacophore features (one acceptor, one donor, and two ring aromatic) along with favorable and unfavorable field effects were demonstrated in PHASE-based pharmacophore model. MM-GBSA analyses revealed significance of different polar, non-polar, and solvation energies. Docking-based alignment of ligands was used to perform Gaussian-based 3D-QSAR study that further demonstrated importance of different field effects. Overall, it was found that polar interactions (hydrogen bonding, bridged hydrogen bonding, and pi–cation) play major roles for higher activity. Steric groups increase the total contact surface area but it should have higher fractional polar surface area to adjust solvation energy. Structure-based pharmacophore mapping spotted the positive ionizable feature of ligands as the most important feature for discriminating highly active compounds from inactives. Molecular dynamics simulation, conducted on highly active ligands, described the dynamic behaviors of the protein complexes and supported the interpretations obtained from other modeling analyses. The current study may be useful for designing PARP-1 inhibitors.     

Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies

Chemical feature based pharmacophore models were generated for Toll-like receptors 7 (TLR7) agonists using HypoGen algorithm, which is implemented in the Discovery Studio software. Several methods tools used in validation of pharmacophore model were presented. The first hypothesis Hypo1 was considered to be the best pharmacophore model, which consists of four features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features. In addition, homology modeling and molecular docking studies were employed to probe the intermolecular interactions between TLR7 and its agonists. The results further confirmed the reliability of the pharmacophore model. The obtained pharmacophore model (Hypo1) was then employed as a query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit was identified as a potent TLR7 agonist, which has antiviral activity against hepatitis virus in vitro. Therefore, our current work provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 agonists with desired biological activity.     

Dynamic and Multi-Pharmacophore Modeling for Designing Polo-Box Domain Inhibitors

The polo-like kinase 1 (Plk1) is a critical regulator of cell division that is overexpressed in many types of tumors. Thus, a strategy in the treatment of cancer has been to target the kinase activity (ATPase domain) or substrate-binding domain (Polo-box Domain, PBD) of Plk1. However, only few synthetic small molecules have been identified that target the Plk1-PBD. Here, we have applied an integrative approach that combines pharmacophore modeling, molecular docking, virtual screening, and in vitro testing to discover novel Plk1-PBD inhibitors. Nine Plk1-PBD crystal structures were used to generate structure-based hypotheses. A common pharmacophore model (Hypo1) composed of five chemical features was selected from the 9 structure-based hypotheses and used for virtual screening of a drug-like database consisting of 159,757 compounds to identify novel Plk1-PBD inhibitors. The virtual screening technique revealed 9,327 compounds with a maximum fit value of 3 or greater, which were selected and subjected to molecular docking analyses. This approach yielded 93 compounds that made good interactions with critical residues within the Plk1-PBD active site. The testing of these 93 compounds in vitro for their ability to inhibit the Plk1-PBD, showed that many of these compounds had Plk1-PBD inhibitory activity and that compound Chemistry_28272 was the most potent Plk1-PBD inhibitor. Thus Chemistry_28272 and the other top compounds are novel Plk1-PBD inhibitors and could be used for the development of cancer therapeutics.     

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.     

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.

3D-QSAR study of Chk1 kinase inhibitors based on docking

Checkpoint kinase 1 (Chk1), a kind of a serine/threonine protein kinase, plays a significant role in DNA damage-induced checkpoints. Chk1 inhibitors have been demonstrated to abrogate the S and G2 checkpoints and disrupt the DNA repair process, which results in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain at the G1 phase via p53 to repair their DNA damages, and are less influenced by the abrogation of S and G2 checkpoint. Therefore, selective inhibitors of Chk1 may be of great therapeutic value in cancer treatment. In this paper, in order to understand the structure-activity relationship of macro-cyclic urea Chk1 inhibitors, a study combined molecular docking and 3D-QSAR modeling was carried out, which resulted in two substructure-based 3D-QSAR models, including the CoMFA model (r(2), 0.873; q(2), 0.572) and CoMSIA model (r(2), 0.897; q(2), 0.599). The detailed microscopic structures of Chk1 binding with inhibitors were performed by molecular docking. Two docking based 3D-QSAR models were developed (CoMFA with r(2), 0.887; q(2), 0.501; CoMSIA with r(2), 0.872; q(2), 0.520). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures would be helpful to better understand the structure-activity relationship. All the conclusions drawn from both the 3D-QSAR contour maps and molecular docking were in accordance with the experimental activity dates. The results suggested that the developed models and the obtained CHk1 inhibitor binding structures might be reliable to predict the activity of new inhibitors and reasonable for the future drug design.     

Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using Catalyst

Cyclooxygenase (COX) enzymes catalyse the biosynthesis of prostaglandins and thromboxane from arachidonic acid (AA). We summarize in this paper, the development of pharmacophores of a dataset of inhibitors for COX-2 by using the Catalyst/Hypogen module using six chemically diverse series of compounds. Training set consisting of 24 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 10000 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, one hydrogen bond donor, one hydrogen bond acceptor, one hydrophobic aliphatic and one ring aromatic feature, had a correlation (r) of 0.954 and a root mean square deviation of 0.894. The entropy (configuration cost) value of the hypotheses was 16.79, within the allowed range. The difference between the null hypothesis and the fixed cost and between the null hypothesis and the total cost of the best hypothesis (hypothesis 1) was 88.37 and 78.51, respectively. The model was validated on a test set consisting of six different series of structurally diverse 22 compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug like molecules to retrieve new chemical entities as potent COX-2 inhibitors. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis. Figure 3D Pharmacophore model generated using structurally diverse COX-2 inhibitors     

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r²?=?0.92, SD?=?0.16, F?=?84.8, N?=?40) and for test set (Q²?=?0.71, RMSE?=?0.06, Pearson R?=?0.90, N?=?10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.