Pharmacophore-based 3D-QSAR Research Articles

Active site fingerprinting and pharmacophore screening strategies for the identification of dual inhibitors of protein kinase C $$(\hbox {PKC}{\upbeta })$$ ( PKC β ) and poly (ADP-ribose) polymerase-1 (PARP-1)

Current clinical studies have revealed that diabetic complications are multifactorial disorders that target two or more pathways. The majority of drugs in clinical trial target aldose reductase and protein kinase C ([Formula: see text]), while recent studies disclosed a significant role played by poly (ADP-ribose) polymerase-1 (PARP-1). In light of this, the current study was aimed to identify novel dual inhibitors of [Formula: see text] and PARP-1 using a pharmaco-informatics methodology. Pharmacophore-based 3D QSAR models for these two targets were generated using HypoGen and used to screen three commercially available chemical databases to identify dual inhibitors of [Formula: see text] and PARP-1. Overall, 18 hits were obtained from the screening process; the hits were filtered based on their drug-like properties and predicted binding affinities (docking analysis). Important amino acid residues were predicted by developing a fingerprint of the active site using alanine-scanning mutagenesis and molecular dynamics. The stability of the complexes (18 hits with both proteins) and their final binding orientations were investigated using molecular dynamics simulations. Thus, novel hits have been predicted to have good binding affinities for [Formula: see text] and PARP-1 proteins, which could be further investigated for in vitro/in vivo activity.

Pharmacophore modeling and 3D-QSAR studies of galloyl benzamides as potent P-gp inhibitors

P-gp transporter regulates key ADME of drugs in MDR condition. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of galloyl benzamides analogs possessing P-gp inhibitory activity. Developed pharmacophore model contains two hydrogen-bond acceptors (A), one hydrophobic (H), one hydrogen-bond donor (D) and two aromatic rings (R). These are crucial molecular fingerprints which predict binding efficacy of high-affinity and low-affinity ligands to the P-gp efflux pump. These pharmacophoric features point toward key structural requirements of galloyl benzamides for potent P-gp inhibition. Furthermore, a biological correlation 3D-QSAR variants and functional fingerprints of P-gp responsible for the receptor binding were observed. Alignment of the developed model with P-gp crystal structure indicated importance of A2 and A4 H-bond acceptor sites, which are involved in the important interactions with Glu530 and His690 residues of the active site. Excellent statistical results of QSAR model such as good correlation coefficient (r 2 > 0.95), higher F value (F > 205) and excellent predictive power (Q 2 > 0.6) with low standard deviation (SD < 0.2) strongly suggest that the developed model is good for the future prediction of P-gp inhibitory activity of new galloyl benzamide analogs.

Pharmacophore-Based 3D-QSAR Modeling, Virtual Screening and Molecular Docking Analysis for the Detection of MERTK Inhibitors with Novel Scaffold.

MERTK plays an important role in cell biology and is correlated with many cancers, such as mantle cell lymphomas, pituitary adenomas, and T-cell acute lympholoblastic leukemia. So identification of new MERTK inhibitors is of extreme importance. In this study, 107 MERTK inhibitors with known activities were gathered to generate a ligand-based pharmacophore model (ADDHH.4), followed by building a 3D-QSAR model, which had high value of coefficient of determination (R(2)=0.9061) and high value of coefficient of determination (Q(2)=0.7442). For the pharmacophore model, two hydrogen bond donors (D), one hydrogen bond receptor (A), and two hydrophobic groups (H) were considered as the key elements contributing to ligand activity. The model then served to search a drug-like database with 1.5 million molecules, and 47832 hits were obtained. Subsequently, docking procedure was applied on these hits, and 840 compounds were obtained through high-throughput virtual screening (HTVS). Standard precision (SP), extra precision (XP) and rule of five were also used in virtual screening protocol. Finally, six candidates were identified as potential MERTK inhibitors, with the docking mode in MERTK analyzed.

Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis

Epidermal growth factor receptor (EGFR) protein tyrosine kinases are over expressed in several human cancers and considered as a promising target for developing novel anticancer drugs. In this study, the ligand-based pharmacophore mapping and atom-based 3D-QSAR approach was carried out on a series of 40 novel pyrrolo[3, 2-d]pyrimidine derivatives acting as EGFR inhibitors. The best pharmacophore hypothesis AAADRR.295 was selected and an atom-based 3D-QSAR model was generated by applying partial least-squares algorithm. The developed model was validated and used as a 3D query in sequential virtual screening study to filter five chemical databases. The obtained compounds were further filtered according to Lipinski rule of five and fitness score. Subsequently, a multistep molecular docking study was employed on the retrieved hits and finally, 12 compounds were prioritized as potential leads against EGFR, which exhibited high docking scores, correlated binding mode to experimentally proven compounds and constructive drug-like properties. The results of this study provide detailed structural insights and emphasize the important binding features of these compounds, which may assists in the design and development of novel EGFR inhibitors.

Identification of Potential MEK1 Inhibitors by Pharmacophore-based Virtual Screening and MD Simulations

MEK proteins play a critical role in tumor proliferation, differentiation, and survival. Hence MEK1 inhibitors are of particular importance in the treatment of related diseases. The present study describes pharmacophore-based 3DQSAR model generation based on 82 known inhibitors of MEK1 allosteric binding cavity. The best pharmacophore model developed consisted of four features, namely AHHR. The model was used as a query to screen the databases of almost 1.3 million compounds. Finally, 8 hits were indentified and the docking study manifested that these compounds interacted with MEK1 well. Finally, 10 ns MD simulations of the obtained ligand-receptor system were performed. The stable binding mode of the system was determined. Our results showed that the identified 8 hits would be useful for the development of potent MEK1 agents. Keywords: 3D-QSAR, Allosteric binding cavity, MD simulations, MEK1, Molecular docking, Virtual screening.

Pharmacophore-Based 3DQSAR and Molecular Docking Studies to Identify New Non-Peptidic Inhibitors of Cathepsin S

Cathepsin S (CatS) is one of the 11 cysteine protease cathepsins which are expressed predominantly in antigen presenting cells (APC) namely B cells, macrophages and dendritic cells. CatS has been implicated in a wide range of diseases such as rheumatoid arthritis, multiple sclerosis, neuropathic pain and allergic disorders. In the present study, pharmacophore mapping studies followed by 3D QSAR analysis was undertaken for a large set of 161 molecules reported to be non-covalent binding and non-peptidic inhibitors of CatS. The activity range (IC50) of these compounds was between 2 picomolar to 100 nanomolar. A five point pharmacophore model with three hydrogen bond acceptors (A), one hydrogen bond donor (D) and one hydrophobic (H) group as pharmacophoric features was developed. The generated model showed reasonable predictive power, with a correlation coefficient Q(2) of 0.607. The model was further confirmed by an external test-set validation that showed statistically significant parameters r(2) value of 0.840 with the R(2)p value of 0.812 and r(2)m value of 0.530. Validated model was then used to identify six diverse non-peptidic scaffolds from a commercial structure database by the analyses of parameters such as pharmacophore fitness, docking score, interacting amino acids and ADME properties to achieve prototypical lead compounds.

Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach

JAK2 and JAK3 are non-receptor protein tyrosine kinases implicated in B-cell- and T-cell-mediated diseases. Both enzymes work via different pathways but are involved in the pathogenesis of common lymphoid-derived diseases. Hence, targeting both Janus kinases together can be a potential strategy for the treatment of these diseases. In the present study, two separate pharmacophore-based 3D-QSAR models ADRR.92 (Q(2)(test)0.663, R(2)(train) 0.849, F value 219.3) for JAK2 and ADDRR.142 (Q(2)(test)0.655, R(2)(train) 0.869, F value 206.9) for JAK3 were developed. These models were employed for the screening of a PHASE database of approximately 1.5 million compounds; subsequently, the retrieved hits were screened employing docking simulations with JAK2 and JAK3 proteins. Finally, ADME properties of screened dual inhibitors displaying essential interactions with both proteins were calculated to filter candidates with poor pharmacokinetic profiles. These candidates could serve as novel therapeutic agents in the treatment of lymphoid-related diseases.

Pyrid-2-yl and 2-CyanoPhenyl fused heterocyclic compounds as human P2X $$_{3}$$ 3 inhibitors: a combined approach based on homology modelling, docking and QSAR analysis

P2X receptors are hetero-oligomeric proteins that function as membrane ion channels and are gated by extracellular ATP. The hP2X[Formula: see text] subunit is a constituent of the channels on a subset of sensory neurons involved in pain signaling, where ATP released by damaged and inflamed tissue can initiate action potentials. Hence, the inhibition of ATP-activated P2X3 receptor is an exciting approach for the treatment of inflammatory and neuropathic pain. Recently, the crystal structures of zebrafish P2X4 (zP2X4) were obtained in closed, apo state (PDB ID: 3I5D) and ATP-bound, open state (PDB ID: 4DW1). These structures were used to develop a homology model of human P2X3 (hP2X3 in order to identify through docking studies, the binding modes of known P2X3 inhibitors and their key active site interactions, along with a pharmacophore-based 3D-QSAR model for a series of 136 Pyrid-2-yl and 2-CyanoPhenyl fused heterocyclic compounds. These 3D-QSAR models have been developed with different combinations of training and test set divisions obtained by random separation, Jarvis-Patrick clustering, K-means clustering and sphere exclusion methods. The best predictive 3D-QSAR model resulted in training set R2 of 0.75, internal test set Q2 of 0.74, Pearson-R value of 0.87 and root mean square error of 0.37. The information generated by the pharmacophore model and docking analyses using the homology model provides valuable clues to design novel potent hP2X3 inhibitors.

Pharmacophore modeling and 3D-QSAR studies of leucettines as potent Dyrk2 inhibitors

Dyrk family enzymes are essential components of important signaling casades in the pathophysiology of cancer and Alzheimer’s disease. Especially, Dyrk2 biological expression levels regulate key signaling processes in these diseases. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of leucettine analogs possessing Dyrk2 inhibitory activity. Developed pharmacophore model contains four hydrogen bond acceptors (A) and one hydrophobic aromatic ring (R). These are crucial molecular fingerprints which predict binding efficacy of high affinity and low affinity ligands to the Dyrk2 enzyme. These pharmacophoric features point toward key structural requirements of leucettines for potent Dyrk2 inhibition. Furthermore, a biological correlation between pharmacophore hypothesis-based 3D-QSAR variables and functional fingerprints of leucettines responsible for the receptor binding was observed. Alignment of the developed model with Dyrk2 crystal structure indicated importance of A3 and A4 H-bond accetor sites, which are involved in the important interactions with Leu231A and Lys178A residues of the active site. Excellent statistical results of QSAR model such as good correlation coefficient (r2 > 0.95), higher F value (F > 106), and excellent predictive power (Q2 > 0.7) with low standard deviation (SD < 0.2) strongly suggest that the developed model is good for the future prediction of Dyrk2 inhibitory activity of new leucettine analogs.

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.

3D-QSAR studies on UDP-glucuronosyltransferase 2B7 substrates using the pharmacophore and VolSurf approaches

UDP-glucuronosyltransferase 2B7 (UGT2B7) is an important enzyme responsible for clearance of many drugs. Here, we report two 3D quantitative structure-activity relationship (QSAR) models for UGT2B7 using the pharmacophore and VolSurf approach, respectively.The dataset included 53 structurally diverse UGT2B7 substrates, 36 of which were used for the training set and 17 of which for the external test set. Pharmacophore-based 3D-QSAR model (or hypothesis) was developed using the Discovery Studio program. A user-defined “glucuronidation site” feature was forcefully included in a pharmacophore hypothesis. VolSurf-based 3D-QSAR model was generated using the VolSurf program. This involves calculation of VolSurf descriptors, variable selection with the FFD algorithm, and partial least squares (PLS) analyses.The best pharmacophore model (r2 = 0.736) consists of one glucuronidation site, one hydrogen bond acceptor, and three hydrophobic regions. Using this model, Km values for 14 of 17 test substrates were predicted within one log unit. The yielded VolSurf (PLS) model with two components shows statistical significance in both fitting and internal predicting (r2 = 0.866, q2 = 0.728). Further, the Km values for all test substrates were predicted within one log unit. In addition, the VolSurf model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include molecular size and shape, integy moments, capacity factors, best volumes of DRY probe, H-bonding, and log P.In conclusion, the pharmacophore and VolSurf approaches are successfully utilized to establish predictive models for UGT2B7. The derived models should be an efficient tool for high throughput prediction of UGT2B7 metabolism.

Pharmacophore-based 3D-QSAR as a predictive method for the QSAR analysis on a series of potent and selective inhibitors for three kinases of RTK family

For targets belonging to the same family of receptors, inhibitors often act at more than one biological target and produce a synergistic effect. Separate pharmacophore-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed from our data-set for the kinase insert domain-containing receptor (KDR), cKit and Flt1 inhibitors. These models showed excellent internal predictability and consistency; validation using test-set compounds yielded a good predictive power for the pIC50-value. The field contour maps (CoMFA and CoMSIA) corresponding to the KDR, cKit and Flt1 kinase subtypes reflected the characteristic similarities and differences between these types. These maps provided valuable information to facilitate structural modifications of the inhibitor to increase selectivity of the KDR over cKit and Flt1.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT 1A receptor ligands

5-HT 1A receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT 1A receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds.

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of hypoxia-inducible factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q(2) = 0.564, r(2)=0.945; CoMSIA: q(2) = 0.575, r(2)=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors.

Dibenzazecine compounds with a novel dopamine/5HT2A receptor profile and 3D-QSAR analysis

BackgroundAntipsychotics are divided into typical and atypical compounds based on clinical efficacy and side effects. The purpose of this study was to characterize in vitro a series of novel azecine-type compounds at human dopamine D1-D5 and 5HT2A receptors and to assign them to different classes according to their dopamine/5HT2A receptor profile.ResultsRegardless of using affinity data (pKi values at D1-D5 and 5HT2A) or selectivity data (15 log (Ki ratios)), principal component analysis with azecine-type compounds, haloperidol, and clozapine revealed three groups of dopamine/5HT2A ligands: 1) haloperidol; 2) clozapine plus four azecine-type compounds; 3) two hydroxylated dibenzazecines. Reducing the number of Ki ratios used for principal component analysis from 15 to two (the D1/D2 and D2/5HT2A Ki ratios) obtained the same three groups of compounds. The most potent dibenzazecine clustering in the same group as clozapine was the non-hydroxylated LE410 which shows a slightly different D2-like receptor profile (D2L > D3 > D4.4) than clozapine (D4.4 > D2L > D3). The monohydroxylated dibenzacezine LE404 clusters in a separate group from clozapine/LE410 and from haloperidol and shows increased D1 selectivity.ConclusionIn conclusion, two compounds with a novel dopamine/5HT2A receptor profile, LE404 and LE410, with some differences in their respective D1/D2 receptor affinities including a validated pharmacophore-based 3D-QSAR model for D1 antagonists are presented.

Pharmacophore-based design, synthesis, biological evaluation, and 3D-QSAR studies of aryl-piperazines as α 1-adrenoceptor antagonists

Phenyl-piperazines were designed and synthesized based on pharmacophore for uro-selective α 1-adrenoceptor antagonists and 3D chemical database searching. Within this series, three compounds, 2, 3, and 13, showed similar or better α 1-AR antagonistic activity compared with prazosin. The 3D-QSAR study of these compounds may provide useful information for the development of novel aryl-piperazines as uro-selective α 1-adrenoceptor antagonists, which can be used for the treatment of BPH with fewer side effects.

Pharmacophore and quantitative structure-activity relationship modeling: complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity.

Pharmacophore, two-dimensional (2D), and three-dimensional (3D) quantitative structure-activity relationship (QSAR) modeling techniques were used to develop and test models capable of rationalizing and predicting human UDP-glucuronosyltransferase 1A4 (UGT1A4) substrate selectivity and binding affinity (as K(m,app)). The dataset included 24 structurally diverse UGT1A4 substrates, with 18 of these comprising the training set and 6 an external prediction set. A common features pharmacophore was generated with the program Catalyst after overlapping the sites of conjugation using a novel, user-defined "glucuronidation" feature. Pharmacophore-based 3D-QSAR (r(2) = 0.88) and molecular-field-based 3D-QSAR (r(2) = 0.73) models were developed using Catalyst and self-organizing molecular field analysis (SOMFA) software, respectively. In addition, a 2D-QSAR (r(2) = 0.80, CV r(2) = 0.73) was generated using partial least-squares (PLS) regression and variable selection using an unsupervised forward selection (UFS) algorithm. Both UGT1A4 pharmacophores included two hydrophobic features and the glucuronidation site. The 2D-QSAR showed the best overall predictivity and highlighted the importance of hydrophobicity (as log P) in substrate-enzyme binding.