Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

2012 ◽  
Vol 90 (8) ◽  
pp. 675-692 ◽  
Author(s):  
Premlata K. Ambre ◽  
Raghuvir R. S. Pissurlenkar ◽  
Evans C. Coutinho ◽  
Radhakrishnan P. Iyer
Keyword(s):  
Hydrogen Bond ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Qsar Models ◽  
Potential Inhibitors

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

Pharmacophore modeling, 3D-QSAR and molecular docking studies of quinazolines and aminopyridines as selective inhibitors of inducible nitric oxide synthase

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Anshika Mittal ◽  
Ritu Arora ◽  
Rita Kakkar
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Inducible Nitric Oxide Synthase ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Acceptor ◽  
Oxide Synthase ◽  
Inducible Nitric Oxide

Pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies have been performed on a dataset of thirty-two quinazoline and aminopyridine derivatives to get an insight into the important structural features required for binding to inducible nitric oxide synthase (iNOS). A four-point CPH (Common Pharmacophore Hypothesis), AHPR.29, with a hydrogen bond acceptor, hydrophobic group, positively charged ionizable group and an aromatic ring, has been obtained as the best pharmacophore model. Satisfactory statistical parameters of correlation ([Formula: see text]) and cross-validated ([Formula: see text]) correlation coefficients, 0.9288 and 0.6353, respectively, show high robustness and good predictive ability of our selected model. The contour maps have been developed from this model and the analysis has provided an interpretable explanation of the effect that various features and substituents have on the potency and selectivity of inhibitors towards iNOS. Docking studies have also been performed in order to analyze the interactions between the enzyme and the inhibitors. Our proposed model can thus be further used for screening a large database of compounds and design new iNOS inhibitors.

scholarly journals QSAR study and molecular docking on indirubin inhibitors of Glycogen Synthase Kinase-3

2013 ◽  
Vol 11 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Luminita Crisan ◽  
Liliana Pacureanu ◽  
Alina Bora ◽  
Sorin Avram ◽  
Ludovic Kurunczi ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Glycogen Synthase ◽  
Statistical Characteristics ◽  
Hydrogen Bond Donor ◽  
Qsar Study ◽  
Substitution Pattern ◽  
Hydrogen Bond Acceptor ◽  
Physico Chemical ◽  
Qsar Models ◽  
Gsk 3Β

AbstractThe current study describes the development of in silico models based on a novel alternative of the MTD-PLS methodology (Partial-Least-Squares variant of Minimal Topologic Difference) developed by our group to predict the inhibition of GSK-3β by indirubin derivatives. The new MTD-PLS methodology involves selection rules for the PLS equation coefficients based on physico-chemical considerations aimed at reducing the bias in the output information. These QSAR models have been derived using calculated fragmental descriptors relevant to binding including polarizability, hydrophobicity, hydrogen bond donor, hydrogen bond acceptor, volume and electronic effects. The MTD-PLS methodology afforded moderate but robust statistical characteristics (R2 Y(CUM) = 0.707, Q2(CUM) = 0.664). The MTD-PLS model obtained has been validated in terms of predictive ability by joined internal-external cross-validation applying Golbraikh-Tropsha criteria and Y-randomization test. The information supplied by the MTD-PLS model has been evaluated against Fujita-Ban outcomes that afforded a statistically reliable model (R2=0.923). Furthermore, the results originated from QSAR models were laterally validated with docking insights that suggested the substitution pattern for the design of new indirubins with improved pharmacological potential against GSK-3β. The new restriction rules introduced in this paper are applicable and provide reliable results in accordance with physico-chemical reality.

scholarly journals Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3204 ◽  
Author(s):  
Yoon Lee ◽  
Gwan-Su Yi
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Correlation Coefficient ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Test Set

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

Validation of TZD Scaffold as Potential ARIs: Pharmacophore Modeling, Atom-based 3D QSAR and Docking Studies

2017 ◽  
Vol 20 (4) ◽  
Author(s):  
Lalita Dahiya ◽  
Manoj Kumar Mahapatra ◽  
Ramandeep Kaur ◽  
Vipin Kumar ◽  
Manoj Kumar
Keyword(s):  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies

Insilico studies, synthesis and antitubercular activity of some novel quinoline – azitidinone derivatives

2020 ◽  
Vol 16 ◽  
Author(s):  
Trupti. S. Chitre ◽  
Kalyani. D. Asgaonkar ◽  
Amrut B. Vikhe ◽  
Shital M Patil ◽  
Dinesh. R. Garud ◽  
...  
Keyword(s):  
Combinatorial Library ◽  
3D Qsar ◽  
Antitubercular Activity ◽  
Structural Features ◽  
Docking Studies ◽  
Quinoline Derivatives ◽  
Qsar Models ◽  
Molecular Modeling Studies ◽  
Lead Generation ◽  
Mcf 7

Background: Diarylquinolines like Bedaquiline have shown promising antitubercular activity by their action of Mycobacterial ATPase. Objective: The structural features necessary for good antitubercular activity for a series of quinoline derivatives were explored through computational chemistry tools like QSAR and combinatorial library generation. In the current study, 3-Chloro-4-(2-mercaptoquinoline-3-yl)-1-substitutedphenylazitidin-2-one derivatives have been designed and synthesized based on molecular modeling studies as anti-tubercular agents. Method: 2D and 3DQSAR analysis was used to designed compounds having quinoline scaffold. The synthesized compounds were evaluated against active and dormant strains of Mycobacterium tuberculosis (MTB) H37 Ra and Mycobacterium bovis BCG. The compounds were also tested for cytotoxicity against MCF-7, A549 and Panc-1 cell lines using MTT assay. Binding affinity of designed compounds was gauged by molecular docking studies. Results: Statistically significant QSAR models generated by SA-MLR method for 2D QSAR exhibited r2 = 0.852, q2 = 0.811and whereas 3D QSAR with SA-kNN showed q2 = 0.77. The synthesized compounds exhibited MIC in the range of 1.38-14.59(µg/ml) .These compounds showed some crucial interaction with MTB Atpase. Conclusion: The present study has shown some promising results which can be further explored for lead generation.

6,9-Dibromo-2a,10b-diphenyl-2a,5,10,10b-tetrahydro-2H,3H-2,3,4a,10a-tetraazabenzo[g]cyclopenta[cd]azulene-1,4-dione monohydrate

2006 ◽  
Vol 62 (5) ◽  
pp. o1754-o1755
Author(s):  
Neng-Fang She ◽  
Sheng-Li Hu ◽  
Hui-Zhen Guo ◽  
An-Xin Wu
Keyword(s):  
Crystal Structure ◽  
Hydrogen Bond ◽  
Hydrogen Bonds ◽  
Water Molecule ◽  
Title Compound ◽  
Supramolecular Structure ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Compound C ◽  
Bifurcated Hydrogen Bond

The title compound, C24H18Br2N4O2·H2O, forms a supramolecular structure via N—H...O, O—H...O and C—H...O hydrogen bonds. In the crystal structure, the water molecule serves as a bifurcated hydrogen-bond acceptor and as a hydrogen-bond donor.

scholarly journals Pharmacophore Modeling and Virtual Screening Studies of Checkpoint Kinase 1 Inhibitors

2009 ◽  
Vol 57 (7) ◽  
pp. 704-709 ◽  
Author(s):  
Jin-Juan Chen ◽  
Ting-Lin Liu ◽  
Li-Jun Yang ◽  
Lin-Li Li ◽  
Yu-Quan Wei ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Pharmacophore Modeling ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1

scholarly journals Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph A. Bauer ◽  
Gisbert Schneider ◽  
Andreas H. Göller
Keyword(s):  
Machine Learning ◽  
Hydrogen Bond ◽  
Hydrogen Bonding ◽  
Training Data ◽  
Free Energies ◽  
Hydrogen Bond Donor ◽  
Chemical Application ◽  
Hydrogen Bond Acceptor ◽  
Donor And Acceptor ◽  
Target Values

Abstract We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol−1 (acceptors), and 2.3 kJ mol−1 (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.

scholarly journals PHYTOCHEMICALS AS POTENTIAL INHIBITORS OF LANOSTEROL 14 Α-DEMETHYLASE (CYP51) ENZYME: AN IN SILICO STUDY ON SIXTY MOLECULES

2020 ◽  
pp. 18-30
Author(s):  
ASHWINI KHANDERAO JADHAV ◽  
PATHAN KAMRAN KHAN ◽  
SANKUNNY MOHAN KARUPPAYIL
Keyword(s):  
Hydrogen Bond ◽  
Candida Albicans ◽  
Docking Study ◽  
Docking Studies ◽  
Antifungal Drugs ◽  
Ergosterol Biosynthesis ◽  
Binding Residue ◽  
Therapeutic Drugs ◽  
Ergosterol Synthesis ◽  
Potential Inhibitors

Lanosterol 14 α-demethylase (CYP51) is a key protein involved in ergosterol biosynthesis of Candida albicans and a crucial target for ergosterol synthesis inhibition. However, in the last two decades drug resistance is reported under clinical situations to most of the prescribed antifungal drugs like azole group of drugs. In this study, molecular docking of sixty plant molecules with Lanosterol 14 α-demethylase protein has been done. The homology modeling tool PHYRE2 was used to predict the structure of Lanosterol 14 α-demethylase. Predicted structure was used for docking studies with sixty plant molecules by using Autodock 1.5.6 cr2™. Among the sixty plant molecules, forty-seven were found to form hydrogen bond and the rest of the plant molecules did not form a hydrogen bond with Lanosterol 14 α-demethylase. Docking study of a library of sixty molecules revealed that 48 plant molecules showed an excellent and good binding affinity with predicted protein model Lanosterol 14 α-demethylase of Candida albicans. The binding residue comparison of docked molecules with that of Ketoconazole revealed, fourteen molecules have similar binding residue. These fourteen molecules may have a similar mode of action as that of Ketoconazole. These molecules should be screened and used to discover new antifungal therapeutic drugs.

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