scholarly journals 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Study of Thieno[3,2-b]Pyrrole-5- Carboxamide Derivatives as LSD1 Inhibitors

2019 ◽  
Author(s):  
Meilan Huang ◽  
yongtao Xu ◽  
Zihao He ◽  
Min Yang ◽  
Hongyi Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Binding Interaction ◽  
Contour Maps

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and become a new target for anticancer drugs. In recent years, the small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamides LSD1 inhibitors using molecular docking, three dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q2=0.783, r2=0.944, r2pred=0.851) and Comparative molecular similarity indices analysis (CoMSIA q2=0.728, r2=0.982, r2pred=0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, the ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

scholarly journals In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: a Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation

2021 ◽  
Vol 12 (4) ◽  
pp. 5100-5115
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Qsar Study ◽  
Determination Coefficient ◽  
Pharmacokinetic Properties

The Chymotrypsin-like protease (3CLpro) is a drug target in the coronavirus because of its role in processing the polyproteins that are translated from the viral RNA. This study applied 3D quantitative structure-activity relationship (3D-QSAR), molecular docking, and ADMET prediction on a series of SARS-CoV 3CLpro inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q2) values of 0.64 and 0.80, the determination coefficient (R2) values of 0.998 and 0.993 and the standard error of the estimate (SEE) values of 0.046 and 0.091, respectively. The acceptable values of the determination coefficient (R2 test) to CoMFA and CoMSIA respectively corresponding to values of 0.725 and 0.690 utilizing a test set of seven molecules prove the high predictive ability of this model. Molecular docking analysis was utilized to validate 3D-QSAR methods and explain the binding site interactions and affinity between the most active ligands and the SARS-CoV 3CLpro receptor. Based on these results, a novel series of compounds were predicted, and their pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, the best-docked candidate molecules were subjected to molecular dynamics (MD) simulation to affirm their dynamic behavior and stability.

scholarly journals Design, Synthesis, Biological Evaluation, and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 387
Author(s):  
Xiangcong Wang ◽  
Moxuan Zhang ◽  
Ranran Zhu ◽  
Zhongshan Wu ◽  
Fanhong Wu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Modeling ◽  
3D Qsar ◽  
Binding Mode ◽  
Biological Evaluation ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Comfa Model ◽  
Qsar Models

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q2 = 0.797 and r2 = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q2 = 0.567 and r2 = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).

In Silico Studies of Piperidine Derivatives as Protein Kinase B Inhibitors through 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

2021 ◽  
Vol 18 ◽  
Author(s):  
Sisi Liu ◽  
Yaxin Li ◽  
Jin Wang ◽  
Xue Rui ◽  
Haobo Tian ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Protein Kinase ◽  
Protein Kinase B ◽  
Similarity Index ◽  
3D Qsar ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
In Silico Studies

Background: Protein kinase B (Akt) is a serine/threonine-protein kinase that drives the diverse physiological process. Akt is a promising therapeutic target, which involves cancer cell growth, survival, proliferation and metabolism. Objective: The study aims to design highly active Akt inhibitors and to elucidate the structural requirements for their biological activity, we analyzed the key binding features and summarized the structural determinants for their bioactivities. Methods: A series of piperidine derivatives have been investigated employing three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulation. Results: The statistics of the comparative molecular field analysis (CoMFA) model (Q2=0.631, R2=0.951) and the comparative molecular similarity index analysis (CoMSIA) model (Q2=0.663, R2=0.966) indicated that our 3D-QSAR model was accurate and reliable. Besides, the stability of receptor-ligand interactions under physiological conditions was then evaluated by molecular dynamics simulation, in agreement with the molecular docking results. Conclusion: Our study provided valuable insights for the discovery of potent Akt inhibitors.

3D-QSAR Assisted Design of Novel 7-Deazapurine Derivatives as TNNI3K Kinase Inhibitors Using Molecular Docking and Molecular Dynamics Simulation

2020 ◽  
Vol 17 (2) ◽  
pp. 155-168
Author(s):  
Pavithra K. Balasubramanian ◽  
Anand Balupuri ◽  
Swapnil P. Bhujbal ◽  
Seung Joo Cho
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
3D Qsar ◽  
Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Activity Relationship ◽  
Dual Function ◽  
Contour Maps ◽  
Structure Activity

Background: Cardiac troponin I-interacting kinase (TNNI3K) is a cardiac-specific kinase that belongs to MAPKKK family. It is a dual-function kinase with tyrosine and serine/threonine kinase activity. Over-expression of TNNI3K results in various cardiovascular diseases such as cardiomyopathy, ischemia/reperfusion injury, heart failure, etc. Since, it is a cardiac-specific kinase and expressed only in heart tissue, it is an ideal molecular target to treat cardiac diseases. The main objective of the work is to study and understand the structure-activity relationship of the reported deazapurine derivatives and to use the 3D-QSAR and docking results to design potent and novel TNNI3K inhibitors of this series. Methods: In the present study, we have used molecular docking 3D QSAR, and molecular dynamics simulation to understand the structure-activity correlation of reported TNNI3K inhibitors and to design novel compounds of deazapurine derivatives with increased activity. Results: Both CoMFA (q2=0.669, NOC=5, r2=0.944) and CoMSIA (q2=0.783, NOC=5, r2=0.965) have resulted in satisfactory models. The models were validated using external test set, Leave-out- Five, bootstrapping, progressive scrambling, and rm2 metrics calculations. The validation procedures showed the developed models were robust and reliable. The docking results and the contour maps analysis helped in the better understanding of the structure-activity relationship. Conclusion: This is the first report on 3D-QSAR modeling studies of TNNI3K inhibitors. Both docking and MD results were consistent and showed good correlation with the previous experimental data. Based on the information obtained from contour maps, 31 novel TNNI3K inhibitors were designed. These designed compounds showed higher activity than the existing dataset compounds.

scholarly journals Computational Investigation of Adenosine 5′- (α, β-methylene)- diphosphate (AMPCP) Derivatives as Ecto-5′-nucleotidase (CD73) Inhibitors by Using 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Jiatong Wen ◽  
Heng Zhang ◽  
Churen Meng ◽  
Di Zhou ◽  
Gang Chen ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Tumor Cells ◽  
Binding Free Energy ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Comfa Model ◽  
Structure Activity

Abstract CD73, as a surface enzyme anchored on the outside of the cell membrane via glycosylphosphatidylinositol (GPI), can convert the AMP in the tumor cell microenvironment into adenosine to promote the growth of tumor cells. It has been overexpressed in many different types of human tumors, such as gastric cancer, pancreatic cancer, liver cancer and other tumor cells. Therefore, targeted inhibitors of CD73 are considered as potential tumor treatment methods. Due to the low bioavailability of nucleoside CD73 inhibitors, it is necessary to develop new inhibitors. In this study, through molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular dynamics (MD) simulations, a series of CD73 inhibitors were calculated and studied to reveal their structure-activity relationships. Through molecular docking studies, explore the possible mode of interaction between inhibitors and protein. Subsequently, a 3D-QSAR model was established by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, the Q2 and R2 values ​​are 0.708 and 0.983, respectively, while for the best CoMSIA model, the Q2 and R2 values ​​are 0.809 and 0.992, respectively. In addition, the stability of the complex formed by the two inhibitors and CD73 was evaluated by molecular dynamics simulation, and the results are consistent with the results of molecular docking and 3D-QSAR research. Finally, the binding free energy was calculated by the surface area method (MM-GBSA), and the results are consistent with the activities that Van Der Waals and Coulomb contribute the most during the binding process of the molecule to the CD73 protein. In conclusion, our research provides valuable information for the further development of CD73 inhibitors.

3D-QSAR Studies of S-DABO Derivatives as Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

2019 ◽  
Vol 16 (8) ◽  
pp. 868-881
Author(s):  
Yueping Wang ◽  
Jie Chang ◽  
Jiangyuan Wang ◽  
Peng Zhong ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Binding Mode ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Hiv 1

Background: S-dihydro-alkyloxy-benzyl-oxopyrimidines (S-DABOs) as non-nucleoside reverse transcriptase inhibitors have received considerable attention during the last decade due to their high potency against HIV-1. Methods: In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) of a series of 38 S-DABO analogues developed in our lab was studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The Docking/MMFF94s computational protocol based on the co-crystallized complex (PDB ID: 1RT2) was used to determine the most probable binding mode and to obtain reliable conformations for molecular alignment. Statistically significant CoMFA (q2=0.766 and r2=0.949) and CoMSIA (q2=0.827 and r2=0.974) models were generated using the training set of 30 compounds on the basis of hybrid docking-based and ligand-based alignment. Results: The predictive ability of CoMFA and CoMSIA models was further validated using a test set of eight compounds with predictive r2 pred values of 0.843 and 0.723, respectively. Conclusion: The information obtained from the 3D contour maps can be used in designing new SDABO derivatives with improved HIV-1 inhibitory activity.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Design of Dual COX-2 and 5-LOX Inhibitors with Iron-Chelating Properties Using Structure-Based and Ligand-Based Methods

2021 ◽  
Vol 18 ◽  
Author(s):  
Jelena Bošković ◽  
Dušan Ružić ◽  
Olivera Čudina ◽  
Katarina Nikolic ◽  
Vladimir Dobričić
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Qsar Analysis ◽  
In Silico Admet ◽  
Cox 2 ◽  
Lox Inhibitors

Background: Inflammation is common pathogenesis of many diseases progression, such as malignancy, cardiovascular and rheumatic diseases. The inhibition of the synthesis of inflammatory mediators by modulation of cyclooxygenase (COX) and lipoxygenase (LOX) pathways provides a challenging strategy for the development of more effective drugs. Objective: The aim of this study was to design dual COX-2 and 5-LOX inhibitors with iron-chelating properties using a combination of ligand-based (three-dimensional quantitative structure-activity relationship (3D-QSAR)) and structure-based (molecular docking) methods. Methods: The 3D-QSAR analysis was applied on a literature dataset consisting of 28 dual COX-2 and 5-LOX inhibitors in Pentacle software. The quality of developed COX-2 and 5-LOX 3D-QSAR models were evaluated by internal and external validation methods. The molecular docking analysis was performed in GOLD software, while selected ADMET properties were predicted in ADMET predictor software. Results: According to the molecular docking studies, the class of sulfohydroxamic acid analogues, previously designed by 3D-QSAR, was clustered as potential dual COX-2 and 5-LOX inhibitors with iron-chelating properties. Based on the 3D-QSAR and molecular docking, 1j, 1g, and 1l were selected as the most promising dual COX-2 and 5-LOX inhibitors. According to the in silico ADMET predictions, all compounds had an ADMET_Risk score less than 7 and a CYP_Risk score lower than 2.5. Designed compounds were not estimated as hERG inhibitors, and 1j had improved intrinsic solubility (8.704) in comparison to the dataset compounds (0.411-7.946). Conclusion: By combining 3D-QSAR and molecular docking, three compounds (1j, 1g, and 1l) are selected as the most promising designed dual COX-2 and 5-LOX inhibitors, for which good activity, as well as favourable ADMET properties and toxicity, are expected.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan
Keyword(s):  
Dengue Fever ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Contour Maps ◽  
Structure Activity ◽  
Bioactive Ligands

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

Sign in / Sign up

Export Citation Format

Share Document