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Secondary Screening: Vina Docking and Ranking by Binding Energy
Juan C. TorresCarolina MontanezGretel MontanezLuzmarie Reyes
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Objective
To perform a secondary screening to identify the using AutoDock Vina.
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Drug Discovery Strategy
Target Analysis Number, quality and distance of
“hot spots’
Primary Sequence Analysis; degree
conservation (NCBI/Swiss-Prot)
3D Structurewww.pdb.org
PyMol
Optimal target (s) for drug
development
BioAssay
Secondary Screening (AutoDock)
Primary Screening:Pharmacophore
Model(Ligand Scout)
High AffinityLead
Compounds
Identification of Top Hits
Identification of Lead Compounds.
(Ranking of binding energies)
. Pharmacophore identification and
Pharmacophore Model Generation (LigandScout)
Further refinement of Pharmacophore
Model
FTmap Chemical probes
cluster number & quality
Therapeutically relevant protein
targets
Biological Problem (Biomedically Relevant Condition or Process)
Drug-like Databases(≈ 9.5 million drugs)Lead-like Database(≈ 1.3 million drugs)
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Part 1: Run the Docking Screening (AutoDock Vina)
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Part 2: Obtain the Results/ Ranking of Top Hits
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Part 3: Analyze Interactions using Auto Dock tools
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Pharmacophore Generation
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Part 4: Possible Model Refinement
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Conclusion
• Our drug model did not have the same chemical features as the one generated and used in the primary screening.
• The initial model can be refined.