A computational study on cannabinoid receptors and potent bioactive cannabinoid ligands: homology modeling, docking, de novo drug design and molecular dynamics analysis

2009 ◽  
Vol 14 (2) ◽  
pp. 257-276 ◽  
Author(s):  
Serdar Durdagi ◽  
Manthos G. Papadopoulos ◽  
Panagiotis G. Zoumpoulakis ◽  
Catherine Koukoulitsa ◽  
Thomas Mavromoustakos
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Homology Modeling ◽  
Cannabinoid Receptors ◽  
De Novo ◽  
Computational Study ◽  
Dynamics Analysis ◽  
De Novo Drug Design

Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies

2020 ◽  
Vol 17 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Drug Design ◽  
Aromatase Inhibitors ◽  
De Novo ◽  
Scale Up ◽  
Geometry Optimization ◽  
Docking Study ◽  
Molecular Docking Study ◽  
De Novo Drug Design ◽  
Molegro Virtual Docker ◽  
Estrogen Production

Background:Aromatase inhibitors emerged as a pivotal moiety to selectively block estrogen production, prevention and treatment of tumour growth in breast cancer. De novo drug design is an alternative approach to blind virtual screening for successful designing of the novel molecule against various therapeutic targets.Objective:In the present study, we have explored the de novo approach to design novel aromatase inhibitors.Method:The e-LEA3D, a computational-aided drug design web server was used to design novel drug-like candidates against the target aromatase. For drug-likeness ADME parameters (molecular weight, H-bond acceptors, H-bond donors, LogP and number of rotatable bonds) of designed molecules were calculated in TSAR software package, geometry optimization and energy minimization was accomplished using Chem Office. Further, molecular docking study was performed in Molegro Virtual Docker (MVD).Results:Among 17 generated molecules using the de novo pathway, 13 molecules passed the Lipinski filter pertaining to their bioavailability characteristics. De novo designed molecules with drug-likeness were further docked into the mapped active site of aromatase to scale up their affinity and binding fitness with the target. Among de novo fabricated drug like candidates (1-13), two molecules (5, 6) exhibited higher affinity with aromatase in terms of MolDock score (-150.650, -172.680 Kcal/mol, respectively) while molecule 8 showed lowest target affinity (-85.588 Kcal/mol).Conclusion:The binding patterns of lead molecules (5, 6) could be used as a pharmacophore for medicinal chemists to explore these molecules for their aromatase inhibitory potential.

Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning

2021 ◽  
Vol 61 (2) ◽  
pp. 621-630
Author(s):  
Sowmya Ramaswamy Krishnan ◽  
Navneet Bung ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design ◽  
Novel Proteins

De novo Drug Design

2010 ◽  
pp. 165-185 ◽  
Author(s):  
Gisbert Schneider ◽  
Markus Hartenfeller ◽  
Ewgenij Proschak
Keyword(s):  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design

Approaching Target Selectivity by De Novo Drug Design

2019 ◽  
Vol 14 (8) ◽  
pp. 791-803 ◽  
Author(s):  
Thomas Fischer ◽  
Silvia Gazzola ◽  
Rainer Riedl
Keyword(s):  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design

scholarly journals REINVENT 2.0: An AI Tool for De Novo Drug Design

2020 ◽  
Vol 60 (12) ◽  
pp. 5918-5922
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design
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