CoMSIA Models Research Articles

Design, synthesis and anti-HIV activity of novel quinoxaline derivatives

In order to design novel anti-HIV agents, pharmacophore modelling, virtual screening, 3D-QSAR and molecular docking studies were performed. Pharmacophore model was generated using 17 structurally diverse molecules using DISCOtech followed by refinement with GASP module of Sybyl X. The best model containing four features; two donor sites, one acceptor atom and one hydrophobic region; was used as a query for virtual screening in NCI database and 6 compounds with Qfit value ≥98 were retrieved. The quinoxaline ring which is the bio-isostere of pteridine ring, retrieved as a hit in virtual screening, was selected as a core moiety. 3D-QSAR was carried on thirty five 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4-carboxamide derivatives. Contour map analysis of best CoMFA and CoMSIA model suggested incorporation of hydrophobic, bulky and electronegative groups to increase potency of the designed compounds. 50 quinoxaline derivatives with different substitutions were designed on basis of both ligand based drug design approaches and were mapped on the best pharmacophore model. From this, best 32 quinoxaline derivatives were docked onto the active site of integrase enzyme and in-silico ADMET properties were also predicted. From this data, synthesis of top 7 quinoxaline derivatives was carried out and were characterized using Mass, 1H-NMR and 13C-NMR spectroscopy. Purity of compounds were checked using HPLC. These derivatives were evaluated for anti-HIV activity on III-B strain of HIV-1 and cytotoxicity studies were performed on VERO cell line. Two quinoxaline derivatives (7d and 7e) showed good results, which can be further explored to develop novel anti-HIV agents.

Active site characterization and structure based 3D-QSAR studies on non-redox type 5-lipoxygenase inhibitors

Structure-based 3D-QSAR study was performed on a class of 5-benzylidene-2-phenylthiazolinones non-redox type 5-LOX inhibitors. In this study, binding pocket of 5-Lipoxygenase (pdb id 3o8y) was identified by manual docking using 15-LOX (pdb id 2p0m) as a reference structure. Additionally, most of the binding site residues were found conserved in both structures. These non-redox inhibitors were then docked into the binding site of 5-LOX. To generate reliable CoMFA and CoMSIA models, atom fit data base alignment method using docked conformation of the most active compound was employed. The q2cv and r2ncv values for CoMFA model were found to be 0.549 and 0.702, respectively. The q2cv and r2ncv values for the selected CoMSIA model comprised four descriptors steric, electrostatic, hydrophobic and hydrogen bond donor fields were found to be 0.535 and 0.951, respectively. Obtained results showed that our generated model was statistically reliable. Furthermore, an external test set validates the reliability of the predicted model by calculating r2pred i.e.0.787 and 0.571 for CoMFA and CoMSIA model, respectively. 3D contour maps generated from CoMFA and CoMSIA models were utilized to determine the key structural features of ligands responsible for biological activities. The applied protocol will be helpful to design more potent and selective inhibitors of 5-LOX.

Synthesis, 3D-QSAR analysis and biological evaluation of quinoxaline 1,4-di-N-oxide derivatives as antituberculosis agents

A series of quinoxaline 1,4-di-N-oxide derivatives variously substituted at C-2 position were synthesized and evaluated for in vitro antimycobacterial activity. Seventeen compounds exhibited potential activity (MIC ⩽6.25μg/mL) against Mycobacterium tuberculosis (H37Rv), in particular the compounds 3d and 3j having an MIC value of 0.39μg/mL. None of the compounds exhibited cytotoxicity when using an MTT assay in VERO cells. To further investigate the structure–activity relationship, CoMFA (q2=0.507, r2=0.923) and CoMSIA (q2=0.665, r2=0.977) models were performed on the basis of antimycobacterial activity data. The 3D-QSAR study of these compounds can provide useful information for further rational design of novel quinoxaline 1,4-di-N-oxides for treatment of tuberculosis.

2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA.

Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r(2)=0.862, q(2)=0.644) and CoMSIA (r(2)=0.851, q(2)=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R(2) train= 0.843, Q(2) LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity.

QSAR modeling and molecular interaction analysis of natural compounds as potent neuraminidase inhibitors.

Different QSAR models of 40 natural compounds as neuraminidase inhibitors (NIs) are developed to comprehend chemical-biological interactions and predict activities against neuraminidase (NA) from Clostridium perfringens. Based on the constitutional, topological and conformational descriptors, R(2) and Q(2) values of the obtained SRA model are 0.931 and 0.856. The R(2) and Q(2) values of the constructed HQSAR and almond models are 0.903 and 0.767, 0.904 and 0.511, respectively. Based on the pharmacophore alignment, R(2) and Q(2) values of the optimal CoMSIA model are 0.936 and 0.654. Moreover, Rtest(2) and Qext(2) of values of SRA, HQSAR, almond and CoMSIA models are 0.611 and 0.565, 0.753 and 0.750, 0.612 and 0.582, 0.582 and 0.571, respectively. So, QSAR models have good predictive capability. They can be further used to evaluate and screen new compounds. Moreover, hydrogen bonds and electrostatic factors have high contributions to activities. To understand molecular interactions between natural compounds and NA from Clostridium perfringens, molecular docking is investigated. The docking results elucidate that Arg266, Asp291, Asp328, Tyr485, Glu493, Arg555, Arg615 and Tyr655 are especially the key residues in the active site of 2bf6. Hydrogen bonds and electrostatics are key factors, which impact the interactions between NIs and NA. So, the influential factors of interactions between NIs and NA in the docking results are in agreement with the QSAR results.

Developing a CoMSIA Model for Inhibition of COX-2 by Resveratrol Derivatives.

Design of selective cyclooxygenase-2 (COX-2) inhibitors is still a challenging task because of active site similarities between COX isoenzymes. To help with this issue, we tried to generate a 3D-QSAR (3 dimensional quantitative structure activity relationships) model that might reflect the essential features of COX-2 active sites. Compounds in a series of resveratrol derivatives inhibitors with reported biological activity against COX-2 were used to construct a predictive comparative molecular similarity indices (CoMSIA) model. A CoMSIA model with acceptable internal and external predictability was developed and employed to design new not yet synthesized molecules with improved activity and selectivity toward COX-2. Finally, molecular docking of the inhibitors in COX-2 active site demonstrated the possible ability of proposed compounds to inhibit COX-2, selectively.

Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure–activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes.

A Combined Quantitative Structure-Activity Relationship Research of Quinolinone Derivatives as Androgen Receptor Antagonists.

Antiandrogens bicalutamide, flutamide and enzalutamide etc. have been used in clinical trials to treat prostate cancer by binding to and antagonizing androgen receptor (AR). Although initially effective, the drug resistance problem will emerge eventually, which results in a high medical need for novel AR antagonist exploitation. Here in this work, to facilitate the rational design of novel AR antagonists, we studied the structure-activity relationships of a series of 2-quinolinone derivatives and investigated the structural requirements for their antiandrogenic activities. Different modeling methods, including 2D MLR, 3D CoMFA and CoMSIA, were implemented to evolve QSAR models. All these models, thoroughly validated, demonstrated satisfactory results especially for the good predictive abilities. The contour maps from 3D CoMFA and CoMSIA models provide visualized explanation of key structural characteristics relevant to the antiandrogenic activities, which is summarized to a position-specific conclusion at the end. The obtained results from this research are practically useful for rational design and screening of promising chemicals with high antiandrogenic activities.

Combined 3D-QSAR, molecular docking, and molecular dynamics study of tacrine derivatives as potential acetylcholinesterase (AChE) inhibitors of Alzheimer's disease.

Acetylcholinesterase (AChE) is one of the key targets of drugs for treating Alzheimer's disease (AD). Tacrine is an approved drug with AChE-inhibitory activity. In this paper, 3D-QSAR, molecular docking, and molecular dynamics were carried out in order to study 60 tacrine derivatives and their AChE-inhibitory activities. 3D-QSAR modeling resulted in an optimal CoMFA model with q(2) = 0.552 and r(2) = 0.983 and an optimal CoMSIA model with q(2) = 0.581 and r(2) = 0.989. These QSAR models also showed that the steric and H-bond fields of these compounds are important influences on their activities. The interactions between these inhibitors and AChE were further explored through molecular docking and molecular dynamics simulation. A few key residues (Tyr70, Trp84, Tyr121, Trp279, and Phe330) at the binding site of AChE were identified. The results of this study improve our understanding of the mechanisms of AChE inhibitors and afford valuable information that should aid the design of novel potential AChE inhibitors. Graphical Abstract Superposition of backbone atoms of the lowest-energy structure obtained from MD simulation (magenta) onto those of the structure of the initial molecular docking model (green).

CoMFA and CoMSIA studies on 6,7-disubstituted-4-phenoxyquinoline derivatives as c-Met kinase inhibitors and anticancer agents

c-Met kinase is a recognized target for the development of small-molecule inhibitors for the treatment of cancer. In this study, a diverse set of 74 c-Met kinase inhibitors consisted of 6,7-disubstituted-4-phenoxyquinoline derivatives were used for CoMFA and CoMSIA (3D QSAR). 3D QSAR models were obtained using rigid body (Distill) alignment of training and test set molecules. CoMFA and CoMSIA models were found statistically significant with leave-one-out correlation coefficients (q 2) of 0.626 and 0.556, respectively, cross-validated coefficients (r cv 2 ) of 0.532 and 0.501, respectively, and conventional coefficients (r 2) of 0.907 and 0.940, respectively. QSAR models were validated by a test set of 23 compounds giving satisfactory predicted correlation coefficients (r pred 2 ) of 0.456 and 0.701 for CoMFA and CoMSIA models, respectively. This study will provide clues to design new compounds as c-Met kinase inhibitors.

3D-QSAR (CoMFA, CoMFA-RG, CoMSIA) and molecular docking study of thienopyrimidine and thienopyridine derivatives to explore structural requirements for aurora-B kinase inhibition

Aurora-B kinase plays a crucial role in cell cycle events and is identified as an important factor in regulation of spindle check point assembly. Thus, it can be proved as an important target in the field of oncology. 3D-QSAR model was generated using 54 molecules reported in literature containing thienopyrimidine and thienopyridine as scaffolds. All molecules were aligned using Distill function in Sybyl X1.2. This generated best model of CoMFA-RG (Region focusing) and CoMSIA were statistically significant with correlation coefficient r2ncv of 0.97, for both & Leave one out coefficient (LOO) q2 of 0.70 and 0.72, respectively. Best CoMSIA model was built up using various combination of descriptors and proved statistical significant among all models. Best CoMFA-RG and CoMSIA models were validated by 12 test set molecules giving satisfactory prediction (r2pred) values of 0.86 and 0.88, respectively. External test set validation was performed using 20 molecules and satisfactory prediction of their biological activity was found. Active compounds were docked on protein (PDB ID: 4C2V) by GOLD module and revealed important interactions with amino acids at ATP-binding region. These data explored insight requirements for Aurora-B inhibition which might be fruitful for understanding mechanisms with kinase ligand interactions.

Synthesis, biological evaluation and 3D-QSAR studies of imidazolidine-2,4-dione derivatives as novel protein tyrosine phosphatase 1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) plays a vital role in the regulation of insulin sensitivity and dephosphorylation of the insulin receptor, so PTP1B inhibitors may be potential agents to treat type 2 diabetes. In this work, a series of novel imidazolidine-2,4-dione derivatives were designed, synthesized and assayed for their PTP1B inhibitory activities. These compounds exhibited potent activities with IC50 values at 0.57–172 μM. A 3D-QSAR study using CoMFA and CoMSIA techniques was carried out to explore structure activity relationship of these molecules. The CoMSIA model was more predictive with q2 = 0.777, r2 = 0.999, SEE = 0.013 and r2pred = 0.836, while the CoMFA model gave q2 = 0.543, r2 = 0.998, SEE = 0.029 and r2pred = 0.754. The contour maps derived from the best CoMFA and CoMSIA models combined with docking analysis provided good insights into the structural features relevant to the bioactivity, and could be used in the molecular design of novel imidazolidine-2,4-dione derivatives.

Analysis of B-Raf[Formula: see text] inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies.

In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf[Formula: see text] inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results [Formula: see text], which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values [Formula: see text] based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained [Formula: see text]. The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf[Formula: see text] kinase.

3D-QSAR studies on 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4- carboxamide derivatives as HIV-1 integrase inhibitors

HIV integrase enzyme is well established potential target for antiretroviral therapy since a decade. Resistance to drugs (Raltegravir and Elvitegravir) used as HIV integrase inhibitors is already been identified and there is an urgent need to discover newer molecules which can overcome this issue. With this aim, ligand based drug design technique, 3D-QSAR, was carried out on a series of 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4-carboxamide derivatives as HIV-1 integrase inhibitors. CoMFA and CoMSIA approaches were used for this study. The total dataset of 35 derivatives were divided into training set (28) and test set (7) in 80:20 ratio. In CoMFA model, the cross validated q2 and the non-cross validated r2 value for training set were found as 0.683 and 0.988, respectively; while in CoMSIA model, q2 value was 0.707 and r2 value was 0.989. Further the most active compound 3 was docked into binding site of integrase enzyme using SurFlex-Dock. The CoMFA and CoMSIA contour maps, MOLCAD-generated surface maps of binding site and docking poses were found complimentary with each other in terms of electrostatic, lipophilicity, and hydrogen-bonding potential. These results were used to design novel quinoxaline derivatives as HIV-1 integrase inhibitors which could be explore in future to identify novel HIV-1 integrase inhibitors.

1,4-Disubstituted aromatic piperazines with high 5-HT2A/D2 selectivity: Quantitative structure-selectivity investigations, docking, synthesis and biological evaluation

Simultaneous targeting of dopamine D2 and 5-HT2A receptors for the treatment of schizophrenia is one key feature of typical and atypical antipsychotics. In most of the top-selling antipsychotic drugs like aripiprazole and risperidone, high affinity to both receptors can be attributed to the presence of 1,4-disubstituted aromatic piperazines or piperidines as primary receptor recognition elements. Taking advantage of our in-house library of phenylpiperazine-derived dopamine receptor ligands and experimental data, we established highly significant CoMFA and CoMSIA models for the prediction of 5-HT2A over D2 selectivity. Subsequently, the models were applied to identify the selective candidates 55–57 from our newly synthesized library of GPCR ligands comprising a pyrazolo[1,5-a]pyridine head group and a 1,2,3-triazole based linker unit. The test compound 57 showed subnanomolar a Ki value (0.64nM) for 5-HT2A and more than 10- and 30-fold selectivity over the dopamine receptor isoforms D2S and D2L, respectively.

Insight into the structural requirements of aminopyrimidine derivatives for good potency against both purified enzyme and whole cells of M. tuberculosis: combination of HQSAR, CoMSIA, and MD simulation studies

The Mycobacterium tuberculosis protein kinase B (PknB) is critical for growth and survival of M. tuberculosis within the host. The series of aminopyrimidine derivatives show impressive activity against PknB (IC50 < .5 μM). However, most of them show weak or no cellular activity against M. tuberculosis (MIC > 63 μM). Consequently, the key structural features related to activity against of both PknB and M. tuberculosis need to be investigated. Here, two- and three-dimensional quantitative structure–activity relationship (2D and 3D QSAR) analyses combined with molecular dynamics (MD) simulations were employed with the aim to evaluate these key structural features of aminopyrimidine derivatives. Hologram quantitative structure–activity relationship (HQSAR) and CoMSIA models constructed from IC50 and MIC values of aminopyrimidine compounds could establish the structural requirements for better activity against of both PknB and M. tuberculosis. The NH linker and the R1 substituent of the template compound are not only crucial for the biological activity against PknB but also for the biological activity against M. tuberculosis. Moreover, the results obtained from MD simulations show that these moieties are the key fragments for binding of aminopyrimidine compounds in PknB. The combination of QSAR analysis and MD simulations helps us to provide a structural concept that could guide future design of PknB inhibitors with improved potency against both the purified enzyme and whole M. tuberculosis cells.

Exploring structural requirements for peripherally acting 1,5-diaryl pyrazole-containing cannabinoid 1 receptor antagonists for the treatment of obesity.

Peripherally acting cannabinoid 1 (CB1) receptor antagonists are considered as potential therapeutics for the treatment of obesity with desired efficacy and reduced central nervous system side effects. A dataset of 72 compounds containing the 1,5-diaryl pyrazole basic skeleton having peripheral CB1 receptor antagonistic activity was utilized for three-dimensional quantitative structure-activity relationship studies. Compounds of the series exhibited high variations in the biological activity and chemical structures. Different types of molecular alignments, such as atom-based, data-based, centroid-based and centroid/atom-based were utilized to develop the best CoMFA model. The best CoMFA model was obtained with a database alignment and the same alignment was further used for the development of a CoMSIA model. The best developed CoMFA model had [Formula: see text] with six components, [Formula: see text] while the best developed CoMSIA model had [Formula: see text] with six components, [Formula: see text] and [Formula: see text] The predictive [Formula: see text] values of these two models showed test set predictions of 0.528 and 0.679 for the best CoMFA and CoMSIA models, respectively. Based on a higher [Formula: see text] value, the CoMSIA model was found to be the best one. The prediction accuracy and reliability of the best developed CoMSIA model have been validated using well-established methods. Using the inputs from the best CoMSIA contour maps, several novel highly selective peripherally acting CB1 receptor antagonists have been designed and reported herein.

QSAR and molecular mechanism analysis of N-substituted oseltamivir derivatives as potent avian influenza H5N1 neuraminidase inhibitors

To understand chemical–biological interactions and predict activities against avian influenza virus, four QSAR models of 32N-substituted oseltamivir derivatives neuraminidase inhibitors (NIs) are constructed in this paper. R2 and Q2 of PLS, HQSAR, CoMSIA and Almond models are 0.950 and 0.846, 0.789 and 0.751, 0.991 and 0.672, 0.917 and 0.762, respectively. In addition, R2test and Q2ext of PLS, HQSAR, CoMSIA and Almond models are 0.897 and 0.787, 0.937 and 0.700, 0.848 and 0.835, 0.932 and 0.851, respectively. Therefore, QSAR models were excellent, robust and had good predictive capability. These models can be further used to evaluate and screen new compounds with a similar structure. Moreover, hydrogen bonds highly contributed to activity, followed by electrostatic and hydrophobic factors. Docking indicated that Arg118, Glu119, Asp151, Arg152, Arg156, Trp178, Glu227, Ser246, Arg292 and Tyr406 were important residues in the active pocket of 2hu4. The main influential factor of interactions between NIs and neuraminidase was the hydrogen bond, followed by electrostatic and hydrophobic factors. Therefore, docking results were unanimous in QSAR results.

Molecular Modeling Studies of Trisubstituted Thiazoles as Cdc7 Kinase Inhibitors through 3D‐QSAR and Molecular Docking Simulation

The cell division cycle 7 (Cdc7) is a serine‐threonine protein kinase which plays an essential role in replication and initiation of DNA synthesis. Overexpression of the Cdc7 in various tumor growths and in cell proliferation makes it a novel target for cancer therapy. In the present study, we have performed 3D quantitative structure–activity relationship (3D‐QSAR) studies on a series of trisubstituted thiazole derivatives targeting Cdc7 kinase. Reasonable CoMFA (q 2 = 0.838, NOC = 5, r 2 = 0.984) and CoMSIA (q 2 = 0.875, NOC = 6, r 2 = 0.979) models were developed. All the developed models were validated using an external validation test set, leave‐out‐five, bootstrapping, progressive samplings, and rm 2 metrics. Both the models exhibited acceptable values on all validation techniques and hence were considered to be robust and predictive. Molecular docking study revealed the crucial interaction of the most active compound (compound 49) with the conserved active site residues. Our results are in line with the previous studies. Contour map analysis revealed the important insights that would help in increasing the potency of the compounds. From these results, we could suggest that bulky positive substitutions are favored in the R2 position of the phenyl ring. Hydrogen‐bond acceptor groups in the R1 position could increase the activity. Hydrogen‐bond donor groups are favored near the amino group of R3 position. Long substitution in the R3 position will reduce the activity and must be avoided. Our results could be used to design more potent trisubstituted thiazole derivative of Cdc7 kinase inhibitors in future.

Structural findings of phenylindoles as cytotoxic antimitotic agents in human breast cancer cell lines through multiple validated QSAR studies

Antimitotic agents are potential compounds for the treatment of breast cancer. Cytotoxicity is one of the parameters required for anticancer activity. A validated comparative molecular modeling study was performed on a set of phenylindole derivatives through R-group QSAR (RQSAR), regression-based and linear discriminant analysis (LDA)-based 2D QSAR studies and kernel-based partial least square (KPLS) analyses as well as CoMSIA 3D-QSAR study. Antiproliferative activities against two breast cancer cell lines (MDA-MB-231 and MCF7) were separately used as dependent variables. The RQSAR analysis highlighted different E-state indices and pharmacophoric requirements of important substitutions. The best 2D-QSAR model is established on the basis of three machine learning tools – MLR, SVM and ANN. The 2D-QSAR models depicted importance of different structural, physicochemical and topological descriptors. While RQSAR analyses demonstrated the fingerprint requirements of various substitutions, the KPLS analyses showed these requirements for the entire molecule. The CoMSIA model further refines these interpretations and reveals how subtle variations in these structures may influence biological activities. Observations of different modeling techniques complied with each other. The current QSAR study may be used to design potential antimitotic agents. It also demonstrates the utilities of different molecular modeling tools to elucidate the SAR.