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Abstract
Objective: The main objective of the study was to develop the Quantitative Structure- Activity Relationship (QSAR) and pharmacophore model by using data obtained from HT-29 cells to develop potent lead molecule for the scientific community.
Materials and Methods: Common pharmacophore model, atom-based 3D-QSAR, and molecular dynamic (MD) simulation were carried out via computational techniques by using 4H-chromene derivatives.
Results: The reliable common pharmacophoric hypothesis, DHH13 was generated and 3.95 survival value was also found. Furthermore, the statistically significant 3D-QSAR model was developed where r2=0.52 was found by using the Partial least squares (PLS) regression method. Phase predicted activity and Log GI50 demonstrated an important atomic position in the structure of ligands to ascertain anti colon cancer activity. Also, MD simulation was carried out between top rank leads targeting IL-6 that provided better binding conformational and complex stability into the active pocket site of the target throughout the MD simulation.
Conclusion: The outcome of this design shows that the pharmacophore model and 3D-QSAR might be helpful for researchers in the field of medicinal chemistry to design and develop potential anti colon cancer compounds.
Keywords: 4H-Chromene, IL-6, colorectal cancer, pharmacophore, 3D-QSAR model, molecular dynamic simulation.
Current Computer-Aided Drug Design
Title:Pharmacophore Generation, Quantitative Structure-Activity Relationship (QSAR), and Molecular Dynamic Simulation of Newly Substituted N-(6- Chloro-3-cyano-4-phenyl-4H-chromen-2-yl)-2-(4-chloro-phenoxy)-acetamide for Anticancer Activity
Volume: 17 Issue: 4
Author(s): Divya Chauhan*, Sushil Kumar, Syed Riaz Hashim and Vinit Raj
Affiliation:
- Department of Chemistry, School of Sciences, IFTM University, Moradabad, Uttar Pradesh,India
Keywords: 4H-Chromene, IL-6, colorectal cancer, pharmacophore, 3D-QSAR model, molecular dynamic simulation.
Abstract:
Objective: The main objective of the study was to develop the Quantitative Structure- Activity Relationship (QSAR) and pharmacophore model by using data obtained from HT-29 cells to develop potent lead molecule for the scientific community.
Materials and Methods: Common pharmacophore model, atom-based 3D-QSAR, and molecular dynamic (MD) simulation were carried out via computational techniques by using 4H-chromene derivatives.
Results: The reliable common pharmacophoric hypothesis, DHH13 was generated and 3.95 survival value was also found. Furthermore, the statistically significant 3D-QSAR model was developed where r2=0.52 was found by using the Partial least squares (PLS) regression method. Phase predicted activity and Log GI50 demonstrated an important atomic position in the structure of ligands to ascertain anti colon cancer activity. Also, MD simulation was carried out between top rank leads targeting IL-6 that provided better binding conformational and complex stability into the active pocket site of the target throughout the MD simulation.
Conclusion: The outcome of this design shows that the pharmacophore model and 3D-QSAR might be helpful for researchers in the field of medicinal chemistry to design and develop potential anti colon cancer compounds.
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Cite this article as:
Chauhan Divya *, Kumar Sushil , Hashim Riaz Syed and Raj Vinit , Pharmacophore Generation, Quantitative Structure-Activity Relationship (QSAR), and Molecular Dynamic Simulation of Newly Substituted N-(6- Chloro-3-cyano-4-phenyl-4H-chromen-2-yl)-2-(4-chloro-phenoxy)-acetamide for Anticancer Activity, Current Computer-Aided Drug Design 2021; 17 (4) . https://dx.doi.org/10.2174/1573409916666200525150410
DOI https://dx.doi.org/10.2174/1573409916666200525150410 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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Computer-aided drug design is a rapidly growing research field that continues to gain momentum, attracting increasing interest from the scientific community. This trend is largely driven by the growing utilization of machine learning and artificial intelligence in drug design and discovery. Artificial Intelligence has proven efficacy across various applications, including ...read more
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