1. Secondary Screening: Vina Docking
and Ranking by Binding Energy
Juan C. Torres
Carolina Montanez
Gretel Montanez
Luzmarie Reyes

2. Objective
To perform a secondary screening to identify the
using AutoDock Vina.

3. Drug Discovery Strategy
Biological Problem
(Biomedically Relevant Condition or Process)
Primary Sequence Optimal target (s)
Analysis; degree for drug
development
conservation
Therapeutically
(NCBI/Swiss-Prot) FTmap Target Analysis
Chemical probes Number, quality
relevant protein
cluster and distance of
targets
number & quality “hot spots’ Pharmacophore
.
3D Structure identification and
www.pdb.org
PyMol Pharmacophore Model
Generation (LigandScout)
Primary Screening:
Pharmacophore Drug-like Databases
Model (≈ 9.5 million drugs)
(Ligand Scout) Lead-like Database
Further refinement (≈ 1.3 million drugs)
of Pharmacophore
Model
Identification of Top Hits
Secondary Screening
(AutoDock)
Identification of
High Affinity Lead Compounds.
BioAssay Lead (Ranking of binding
Compounds energies)

4. Part 1: Run the Docking Screening
(AutoDock Vina)

5. Part 2: Obtain the Results/ Ranking of
Top Hits

7. Part 3: Analyze Interactions using Auto
Dock tools

8. Pharmacophore Generation

9. Part 4: Possible Model Refinement

11. 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.