Development Of 3D-QSAR Models Research Articles

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches.

Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< -9.0kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

Anti-HIV Potential of Beesioside I Derivatives as Maturation Inhibitors: Synthesis, 3D-QSAR, Molecular Docking and Molecular Dynamics Simulations.

HIV-1 maturation is the final step in the retroviral lifecycle that is regulated by the proteolytic cleavage of the Gag precursor protein. As a first-in-class HIV-1 maturation inhibitor (MI), bevirimat blocks virion maturation by disrupting capsid-spacer peptide 1 (CA-SP1) cleavage, which acts as the target of MIs. Previous alterations of beesioside I (1) produced (20S,24S)-15ꞵ,16ꞵ-diacetoxy-18,24; 20,24-diepoxy-9,19-cyclolanostane-3ꞵ,25-diol 3-O-3',3'-dimethylsuccinate (3, DSC), showing similar anti-HIV potency compared to bevirimat. To ascertain the binding modes of this derivative, further modification of compound 1 was conducted. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis combined with docking simulations and molecular dynamics (MD) were conducted. Five new derivatives were synthesized, among which compound 3b showed significant activity against HIV-1NL4-3 with an EC50 value of 0.28 µM. The developed 3D-QSAR model resulted in great predictive ability with training set (r2 = 0.99, q2 = 0.55). Molecular docking studies were complementary to the 3D-QSAR analysis, showing that DSC was differently bound to CA-SP1 with higher affinity than that of bevirimat. MD studies revealed that the complex of the ligand and the protein was stable, with root mean square deviation (RMSD) values &lt;2.5 Å. The above results provided valuable insights into the potential of DSC as a prototype to develop new antiviral agents.

Dual aromatase-steroid sulfatase inhibitors (DASI's) for the treatment of breast cancer: a structure guided ligand based designing approach

Dual aromatase-steroid sulfatase inhibitors (DASIs) lead to significant deprivation of estrogen levels as compared to a single target inhibition and thereby exhibited an additive or synergistic effect in the treatment of hormone-dependent breast cancer (HDBC). Triazole-bearing DASI’s having structural features of clinically available aromatase inhibitors are identified as lead structures for optimization as DASI’s. To identify the spatial fingerprints of target-specific triazole as DASI’s, we have performed molecular docking assisted Gaussian field-based comparative 3D-QSAR studies on a dataset with dual aromatase-STS inhibitory activities. Separate contours were generated for both aromatase and steroid sulphates showing respective pharmacophoric structural requirements for optimal activity. These developed 3D-QSAR models also showed good statistical measures with the excellent predictive ability with PLS-generated validation constraints. Comparative steric, electrostatic, hydrophobic, HBA, and HBD features were elucidated using respective contour maps for selective target-specific favourable activity. Furthermore, the molecular docking was used for elucidating the mode of binding as DASI’s along with the MD simulation of 100 ns revealed that all the protease-ligand docked complexes are overall stable as compared to reference ligand (inhibitor ASD or Irosustat) complex. Further, the MM-GBSA study revealed that compound 24 binds to aromatase as well as STS active site with relatively lower binding energy than reference complex, respectively. A comparative study of these developed multitargeted QSAR models along with molecular docking and dynamics study can be employed for the optimization of drug candidates as DASI’s. Communicated by Ramaswamy H. Sarma

3D-QSAR assisted identification of selective CYP1B1 inhibitors: an effective bioisosteric replacement/molecular docking/electrostatic complementarity analysis.

Cytochrome P450-1B1 is a majorly overexpressed drug-metabolizing enzyme in tumors and is responsible for inactivation and subsequent resistance to a variety of anti-cancer drugs, i.e., docetaxel, tamoxifen, and cisplatin. In the present study, a 3D quantitative structure-activity relationship (3D-QSAR) model has been constructed for the identification, design, and optimization of novel CYP1B1 inhibitors. The model has been built using a set of 148 selective CYP1B1 inhibitors. The developed model was evaluated based on certain statistical parameters including q2 and r2 which showed the acceptable predictive and descriptive capability of the generated model. The developed 3D-QSAR model assisted in understanding the key molecular fields which were firmly related to the selective CYP1B1 inhibition. A theoretic approach for the generation of new lead compounds with optimized CYP1B1 receptor affinity has been performed utilizing bioisosteric replacement analysis. These generated molecules were subjected to a developed 3D-QSAR model to predict the inhibitory activity potentials. Furthermore, these compounds were scrutinized through the activity atlas model, molecular docking, electrostatic complementarity, molecular dynamics, and waterswap analysis. The final hits might act as selective CYP1B1 inhibitors which could address the issue of resistance. This 3D-QSAR includes several chemically diverse selective CYP1B1 receptor ligands and well accounts for the individual ligand's inhibition affinities. These features of the developed 3D-QSAR model will ensure future prospective applications of the model to speed up the identification of new potent and selective CYP1B1 receptor ligands.

Identify promising IKK-β inhibitors: A docking-based 3D-QSAR study combining molecular design and molecular dynamics simulation

Inhibitor of nuclear factor kappa B kinase subunit beta (IKK-β), a specific regulator of nuclear factor-κB (NF-κB), is considered a valid target to design novel drugs to treat rheumatoid arthritis, glomerulonephritis and various cancers. In this study, in order to design and then identify promising compounds targeting IKK-β, a series of reported IKK-β inhibitors were used to develop 3D-QSAR models. Docking-based and minimization-based poses were generated for model construction. CoMSIA model #8 based on docking poses was selected due to its satisfactory internal and external validation results and the sufficient information delivery capability. After a contour map analysis, 41 new designs were depicted based on a graphical design scheme and 25 of them were assessed as eligible for screening. Compound 21MX007 has aroused our attention for its both competitive QSAR-prediction and docking-scoring result. Detailed docking interactions of 21MX007-protein complex were investigated via a deep analysis of docking results and a comparative molecular dynamics simulation. Strong interactions and an extra hydrogen bond which echoes the H-bond requirements of substituent acquired from the design scheme were observed. From MD analysis, 21MX007-protein system was tested. The system was proved to have good stability in terms of a downward trend of RMSD and Rg values and a continuous and stable H-bond interaction and a lower average binding free energy. Thus, compound 21MX007 was successfully identified as a promising IKK-β inhibitor.

Discovery of Novel Antimalarial Drugs Based on Thiosemicarbazone Derivatives: An In Silico Approach

Background: Thiosemicarbazones belong to the group of semicarbazides, which contains a sulfur atom instead of an oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi, and other parasites. Objective: The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. Methods: The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training, and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been used for the virtual screening study. Results: DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 = 0.91 and Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids, ARG-265, PHE-227, and LEU-531, required for activity. The developed pharmacophore model had been used for the screening of ZINC compounds, where ZINC26244107, ZINC13469100, ZINC01290725, and ZINC01350173 showed the best XP docking scores (-11.60, -11.27, -11.35, -10.52, respectively) while binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID:5TBO. The docking study was further evaluated by taking standard drug chloroquine, showing similar binding interactions as shown by other compounds. The ADME studies showed the drug-likeness properties of the compounds. Conclusion: The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.

Molecular dynamics and 3D-QSAR studies on indazole derivatives as HIF-1α inhibitors

Hypoxia-inducible factor (HIF) is a transcriptional factor which plays a crucial role in tumour metastasis thereby responsible for development of various forms of cancers. Indazole derivatives have been reported in the literature as potent HIF-1α inhibitor via interaction with key residues of the HIF-1α active site. Taking into consideration the role HIF-1α in cancer and potency of indazole derivative against HIF-1α; it was considered of interest to correlate structural features of known indazole derivatives with specified HIF-1α inhibitory activity to map pharmacophoric features through Three-dimensional quantitative structural activity relationship (3D-QSAR) and pharmacophore mapping. Field and Gaussian based 3D-QSAR studies were performed to realize the variables influencing the inhibitory potency of HIF-1α inhibitors. Field and Gaussian- based 3D-QSAR models were validated through various statistical measures generated by partial least square (PLS). The steric and electrostatic maps generated for both 3D-QSAR provide a structural framework for designing new inhibitors. Further; 3D-maps were also helpful in understanding variability in the activity of the compounds. Pharmacophore mapping also generates a common five-point pharmacophore hypothesis (A1D2R3R4R5_4) which can be employed in combination with 3D-contour maps to design potent HIF-1α inhibitors. Molecular docking and molecular dynamics (MD) simulation of the most potent compound 39 showed good binding efficiency and was found to be quite stable in the active site of the HIF-1α protein. The developed 3D-QSAR models; pharmacophore modelling; molecular docking studies along with the MD simulation analysis may be employed to design lead molecule as selective HIF-1α inhibitors for the treatment of Cancer.

Anticancer evaluation of the selected tetrahydropyrimidines: 3D-QSAR, cytotoxic activities, mechanism of action, DNA, and BSA interactions

Selected tetrahydropyrimidines (THPMs) were investigated by means of cytotoxic activities on selected cancer (HeLa, A549, and LS174) and non-cancerous cell lines (MRC-5). Among evaluated compounds, two of them (B7 and B8) showed good cytotoxic activity on the tested cell lines and were selected for further evaluation that included mechanism of action, DNA and BSA interactions and molecular docking study. Calculated parameters from fluorescence quenching studies indicated that B7 and B8 bind on minor groove of DNA and have great ability to bind on carrier protein. Three-dimensional quantitative structure anti-HeLa activity study was performed with data set of THPMs. Molecular Interaction Fields were used to derive Grid independent descriptors (GRIND), as independent variables in Pentacle software. The quality and predictive capacity of the model was proved by internal statistical parameters: R2 = 0.992, Q2= 0.51, as well as external parameters such as R2pred = 0.804 and rm2, r/2 m and rm2¯, that were higher than 0.5. The structural determinants significant for anti-HeLa activity of compounds were identified by using developed 3D-QSAR model. Interpretation of the most impactful GRIND variables on the anti-HeLa activity generated several hypotheses for design of novel and more potent anti-HeLa tetrahydropyrimidines. Additional molecular targets for the most active synthesized derivatives (B7 and B8) are predicted by use of online web-based tool-SwissTargetPrediction.

The investigation of influencing factors on the degradation of sulfonamide antibiotics in iron-impregnated biochar-activated urea-hydrogen peroxide system: A QSAR study

Iron-impregnated biochar-activated urea-hydrogen peroxide (FB-activated UHP) is a potential in-situ technology for simultaneously reducing soil sulfonamide antibiotic contaminants and improving soil fertility. To better understand the degradation of sulfonamide antibiotics by FB-activated UHP, a two-dimensional quantitative structure-activity relationship (2D-QSAR) model based on quantum chemical parameters and a three-dimensional QSAR (3D-QSAR) model based on molecular force field were developed to investigate the factors influencing the removal efficiencies (Re%). The optimal 2D-QSAR model was Re%= 0.858–8.930 E-5 EB3LYP-0.175 f(+)x with the evaluation indices of R2= 0.732, q2= 0.571, and Qext2= 0.673. The given 2D-QSAR model indicated that the molecular size (EB3LYP) and Fukui index with respect to nucleophilic attack (f(+)) were intrinsic factors influencing Re%. Three degradation pathways were subsequently proposed based on the f(+) distribution. Compared to the 2D-QSAR model, the developed 3D-QSAR model exhibited a better predictive ability, with the evaluation indices of R2= 0.989, q2= 0.696, and SEE= 0.001. The analysis of field contribution rates suggested that electrostatic field (48.2%), hydrophobic field (25.3%), and hydrogen-bond acceptor field (12.7%) were the main factors influencing Re%. These findings generated critical information for evaluating the degradation mechanisms/rules and provided theoretical bases for initially estimating the Re% of sulfonamide antibiotics undergoing FB-activated UHP process.

Insights into the structural requirements of triazole derivatives as promising DPP IV inhibitors: computational investigations

Diabetes is one of the leading causes of death globally as per World Health Organization 2019. To cope up with side effects of current diabetes therapy, researchers have found several novel targets for the treatment of diabetes. Currently, dipeptidyl peptidase IV (DPP IV) has emerged as a target in modulating the diabetes physiology. In the present work, various 3D-Quantitative structure activity relationship (QSAR) techniques namely comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, topomer CoMFA and molecular hologram QSAR are used to explore the structural requirements of triazole derivatives as DPP IV inhibitors. Different models generated by 3D QSAR studies had acceptable statistical values for further prediction of molecules. From the contour maps of QSAR results, important structural features are deduced. Substitutions on N1 and N2 of triazole ring with H-bond donor group enhances the biological activity. Aliphatic side chain, less bulky group, H-bond donor group and –COOH group on N3 of triazole ring are vital for the DPP IV inhibition. Moreover, electron withdrawing side chain on the triazole ring improves the biological activity. Further, novel triazole derivatives were designed and docking results of these compounds proved the efficiency of the developed 3D QSAR model. In future, results of this study may provide promising DPP IV inhibitors for the treatment of diabetes. Communicated by Ramaswamy H. Sarma

Development of 3D-QSAR models for predicting the activities of chemicals to stimulate muscle growth via β2-adrenoceptor

β2-adrenoceptor (β2AR) agonists can stimulate skeletal muscle growth. Their illegal use in food-producing animals, human athletes and bodybuilders causes adverse health effects. In the present study, we developed 3D-QSAR models for predicting the activity of chemicals which can stimulate skeletal muscle growth through β2AR. The activity of 25 β2AR agonists was measured by β2AR-cAMP response element (CRE) –luciferase (Luc) reporter assay. The 3D-QSAR models were built using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models displayed high external predictability (R2 0.996 and 0.992, respectively) and good statistical robustness, and revealed that electrostatic effects were the most prominent forces influencing the activity of β2AR agonists. The CoMFA and CoMSIA contour plots provided clues regarding the main chemical features responsible for the activity variations and also resulted in predictions which correlate very well with the observed activity. In vitro study with differentiated myotubes showed that the potency orders of β2AR agonists in activating the β2AR-CRE-Luc reporter and in upregulating CREB target genes related to muscle growth were consistent. These 3D-QSAR models provide tools for predicting the activity of chemicals which might be illegally used in livestock or humans to stimulate skeletal muscle growth.

Understanding structural characteristics of PARP-1 inhibitors through combined 3D-QSAR and molecular docking studies and discovery of new inhibitors by multistage virtual screening

Poly(ADP-ribose) polymerase (PARP) belongs to family of nuclear proteins having a critical role in base excision DNA repair system by catalysing the process of poly(ADP-ribosyl)ation leading to cell proliferation. The role of PARP inhibitors as anticancer agents was accepted after the approval of olaparib for the treatment of BRCA mutation-induced ovarian cancer. A combined ligand-based and receptor-based approach has been reported here for identification of PARP-1 inhibitors. Seventy one analogues of phthalazinone and 4-carboxamide benzimidazole showing activities against PARP-1 were used to develop ligand-based pharmacophore hypothesis using PHASE. The five-point pharmacophore hypothesis ADHRR.1031 was developed, and atom-based 3D-QSAR studies were carried. The developed 3D QSAR model has good correlation coefficient value (R2 = 0.952) and cross validated with correlation coefficient value (Q2 = 0.764). To further ascertain the structural requirement for PARP-1 inhibition, molecular interaction studies of inhibitors with PARP-1 were carried out by performing docking studies using Glide 5.6. Hydrogen bond interaction with Gly202 and Ser243 is an important interaction observed by the carboxyl amide group. The pharmacophore model depicts the importance of the hydrogen bond donor and acceptor nature of the carboxyl amide group. Further multistage virtual screening was performed to screen molecules with possible PARP-1 inhibition. Initial screening was performed based on pharmacophoric features followed by docking that consisted of HTVS, SP, and XP docking and final optimisation by Prime MM-GBSA calculation. The screened molecules were further scrutinised for their ADME-toxicity properties.

Fragment-Based Drug Design of Selective HDAC6 Inhibitors.

Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors-vemurafenib, venetoclax, and erdafitinib,FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).

Insights into the pharmacophore-based 3D-QSAR modeling, molecular dynamics simulation studies of certain dihydroxy pyrrolidine/piperidine and aza-flavanone derivatives as α-glucosidase inhibitors

Diabetes mellitus is a metabolic disorder and causes relentless health problems, morbidity and mortality to human population around the globe. Acarbose, voglibose and miglitol are some of the eminent glucosidase inhibitors used to control diabetes mellitus. Even though, these eminent and clinically accepted inhibitor's usage was limited because of their side effects. Consequently, there still is a need to extend a safer therapy for diabetes mellitus. Current research focuses on small molecule discovery with an aim to treat a number of diseases and disorders through target-based action. Especially, dihydroxy pyrrolidine/piperidine and aza-flavanones are pharmacologically significant scaffolds in drug discovery research and are also well evaluated for their α-glucosidase inhibitory activity. In the present study conducted, an attempt was made to explore the pharmacophoric features of several α-glucosidase inhibitors through computational approach. An atom-based 3D-QSAR technique was utilized to approve the features that are obtained from 76 compounds. Spatial arrangement and structural features required for α-glucosidase inhibition was analyzed through the developed 3D-QSAR models. A 3D model structure was reproduced for target protein to assess the binding properties of the ligands with protein through homology modelling techniques. Molecular dynamics simulations were performed to understand the conformational fluctuations and stability of ligands at the active pocket of the enzyme. In future, these in silico results will aid the discovery of α-glucosidase inhibitors with good pharmacophoric features and potency.

Atom-based 3D-QSAR, molecular docking, DFT, and simulation studies of acylhydrazone, hydrazine, and diazene derivatives as IN-LEDGF/p75 inhibitors

Since HIV-1 integrase makes use of host genome machinery to accomplish the replication process, where LEDGF/p75 (a cellular cofactor) executes in the lentiviral integration process by interacting with integrase. Thus, the integrase-LEDGF/p75 interaction has become an interesting drug target in developing a potent agent. The purpose of the present study is to understand the inhibition mechanism with a structural basis of developed integrase inhibitors against viral infection. Herein, the computational approaches like atom-based 3D-QSAR, docking, MM/GBSA, DFT, and MDS were applied on a series of acylhydrazone, hydrazine, and diazene derivatives as integrase inhibitors. The developed 3D-QSAR model resulted in great predictive ability with training set (R2 = 0.98, SD = 0.07) and for test set (Q2 = 0.89, RMSE = 0.14, Pearson R = 0.90). The binding mode of interaction and involvement of energy on most and least active compounds into the LEDGF/p75 binding pocket of integrase were explored. We also observed that the predicted 3D-QSAR model has a good level of potential support by means of favorable and unfavorable regions of hydrogen bond donor, acceptor (electron-withdrawing), and hydrophobic groups for most active compound 7. This approach helps further to design anti-HIV inhibitors based on binding mode interaction and stability analysis.

Identify of promising isoquinolone JNK1 inhibitors by combined application of 3D-QSAR, molecular docking and molecular dynamics simulation approaches

JNK1 has been suggested to play a key mediator in metabolic disorders, especially been considered to be a drug target for treatment of type-2 diabetes in recent years. Therefore, inhibiting the activity of JNK1 is a promising way for drug development. In the study, in order to design and identify the potential compounds targeting JNK1, a series of isoquinolone analogs were used to develop 3D-QSAR models. Here, two methods of docking-based and minimization-based were adopted for generation of molecular poses and the subsequent model construction. CoMFA model #5 and CoMSIA model #6 based on docking method were selected due to their informative and satisfactory internal and external validation results. An in-house library containing 113 new designs was built based on contour maps analyzes and then designated for screening. Compound 20JX82 was focused for its preferable predicted activity and docking score. Detailed interactions with JNK1 of compound 20JX82 was investigated via deep analyze of docking results and molecular dynamics simulation. Strong interactions and extra hydrogen bonds which were consistent with the summarized design principles were observed. From MD analyzes, greater stability of the 20JX82 bound complex was found when using sole protein as reference. Thus, compound 20JX82 was successfully identified as promising JNK1 inhibitor.

Three-dimensional Quantitative Structure-activity Relationship (3DQSAR) and Molecular Docking Study of 2-((pyridin-3-yloxy)methyl) Piperazines as α7 Nicotinic Acetylcholine Receptor Modulators for the Treatment of Inflammatory Disorders.

Comparative molecular field analysis (CoMFA) of 27 analogues of 2-((pyridin-3-yloxy)methyl)piperazine derivatives was carried out using software Tripos SYBYL X. Optimal r2 (0.854) and q2 (0.541) values were obtained for the developed 3D-QSAR model. The contour plots obtained from CoMFA analysis have shown 13.84% steric contribution and 66.14% electrostatic contribution towards an anti-inflammatory activity. The homology model of the receptor protein, α7 nicotinic acetylcholine, was generated in SWISS MODELLER using auto template mode and was analysed for the quality using Procheck, QMEAN Z-score, Anolea and GROMOS plots. The QMEAN score for the model was observed to be - 3.862. The generated model of alpha 7 nicotinic acetylcholine receptor was used for docking study of 27 piperazine analogues using Auto-Dock 4.2.5.1. The dock score obtained from docking analysis was then correlated with experimental pIC50 values for in-silico validation of the developed CoMFA model and a good correlation was obtained with correlation coefficient (r2) value of -0.7378. The present investigation suggests an optimal 3D-QSAR with CoMFA model for further evaluating new chemical entities based on piperazine skeleton.

Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis.

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

Docking Based 3D-QSAR Modelling of the Thiophene Analogues as a PTP1B Inhibitors

PTP1B inhibitors have been largely studied because of their promising therapeutic effects, PTP1B dephosphorylate (removal of Phosphate group) the insulin receptor (IR), Insulin Receptor Substrate (IRS), Janus Kinase-2(JAK2) & STAT3 which is present in the downstream of insulin and leptin receptor respectively. PTP1B inhibitors are used for the treatment of diabetes and obesity as a singly drug therapy due to simultaneously acting in both insulin and leptin signalling pathway. The clinical use of PTP1B inhibitors was restricted Selectivity is one of the major issues in the development of PTP1B inhibitors as drugs. Because all PTPs share a high degree of structural conservation in the active site, the pTyr (phosphotyrosine)-binding pocket, The various research results indicate that there are sub pockets adjacent to the PTP active site that can also be targeted for inhibitor development. These studies also provide a molecular basis for addressing and manipulating PTP inhibitor potency and specificity, and suggest a novel paradigm for the design of potent and specific PTP inhibitors Therefore, this work deals with the development of 3D-QSAR models, supported by molecular docking studies, to identify the key requirements underlying selective PTP1B inhibition. Our structure-based effort resulted in significant improvements in potency and selectivity. Docking based alignments and 3D-QSAR study have been performed on 46 molecules based on Thiophene ring scaffold derived from published literature. The QSAR model was selected having highest value of Q2 (0.6005) and Pearson-r (0.8539). The selected model also displayed the highest values of R2 (0.9750) and F-value (179.1) and the lowest SD (0.2230). The contour plots developed on the basis of the best model helped to reveal the essential structural features of Thiophene derivatives responsible for inhibition of PTP1B. The generated model and information revealed from it was utilized to design and predict new congeneric molecules that can be used as potential therapeutic agents.