Virtual Drug Screening for Viral Polymerase

ISEF Category: Biochemistry

Subcategory: Medicinal Biochemistry  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A medicine already on pharmacy shelves can sometimes look like a brand-new antiviral on a computer screen. That is the promise of drug repurposing. You test whether FDA-approved drugs may fit a viral polymerase, the enzyme that helps the virus copy itself. Then you check whether the best-looking hits still hold up when the protein starts moving.

What Is It?

Virtual screening is a computer way to sort through many molecules and ask, which ones seem likely to bind a target protein? Think of it like trying keys in a lock, except the lock is flexible and the keys can twist. AutoDock Vina gives each drug a binding score, which is a rough guess of how well it fits the site.

Consensus scoring goes one step further. Instead of trusting one score, you compare several scoring methods or filters and look for drugs that keep ranking near the top. Molecular dynamics, or MD, then acts like a stress test. It lets you see whether the protein-drug complex stays together as atoms move over time, which matters because a good docking pose can still fall apart in a more realistic simulation.

Why This Is a Good Topic

This makes a strong science fair topic because you can test a real biological question with public data and clear numbers. It connects to antiviral discovery, but you can do the work without a wet lab if you use careful structure prep, docking, and simulation analysis. You also learn skills that matter in research, like data cleanup, ranking methods, control design, and basic statistics.

Research Questions

• How does consensus scoring change the ranking of FDA-approved drugs compared with AutoDock Vina alone?
• What is the effect of target choice, SARS-CoV-2 polymerase versus influenza polymerase, on the top-ranked repurposing candidates?
• Does adding receptor flexibility or ensemble docking change which drugs stay in the top 10?
• To what extent do MD stability metrics agree with docking rank for the strongest hits?
• Which drug properties, such as molecular weight, polarity, or ring count, best predict a high consensus score?
• How does changing the size of the screening library affect the number of stable candidates?

Basic Materials

• Laptop or desktop computer with at least 16 GB of RAM.
• Stable internet access for downloading structures and compound files.
• Python 3.11 with scientific libraries.
• AutoDock Vina installed locally.
• RDKit for compound cleanup and descriptor calculation.
• Open Babel for file conversion between chemical formats.
• Protein structure files from the RCSB Protein Data Bank.
• Spreadsheet software or a notebook for tracking scores and filters.

Advanced Materials

• Workstation with a modern multicore CPU and optional GPU support.
• OpenMM installed for molecular dynamics runs.
• Protein preparation tools for protonation, missing atoms, and alternate conformations.
• A curated FDA-approved compound library from DrugBank or PubChem.
• High-capacity storage for trajectory files and intermediate outputs.
• Visualization software for protein-ligand poses and MD trajectories.
• Reference antiviral ligands and decoy compounds for benchmarking.

Software & Tools

• AutoDock Vina: Scores how well each drug fits the chosen polymerase binding site.
• Open Babel: Converts structures between common chemistry file formats and fixes basic compatibility issues.
• RDKit: Filters compounds, calculates descriptors, and helps you compare chemical features across hits.
• OpenMM: Runs molecular dynamics so you can test whether top docking poses stay stable.
• Python: Organizes the workflow, plots results, and compares ranking methods.

Experiment Steps

1. Define the exact polymerase target, binding site, and comparison set you will study.
2. Build a clean FDA-approved drug library and remove duplicates, salts, and obviously broken structures.
3. Choose one docking workflow, then add a second scoring rule so you can compare rankings instead of trusting one number.
4. Set up controls that include known antivirals and decoy compounds to check whether your screen can recover expected results.
5. Run docking on the full library, then select the top candidates for a stability check in MD.
6. Compare docking rank, MD stability metrics, and drug properties to see which signals agree and which ones conflict.

Common Pitfalls

• Docking into the wrong chain or pocket, which makes the scores meaningless for your target.
• Skipping protonation and charge cleanup, which changes predictions for the same drug.
• Comparing scores from different prepared protein structures, which adds noise that looks like a real effect.
• Treating the top docking score as a final answer, which ignores whether the complex falls apart in simulation.
• Running MD on only one hit, which leaves you with no way to tell if the result is unusual or random.

What Makes This Competitive

A stronger version of this project compares more than one target, more than one scoring method, and more than one stability metric. You can raise the bar by using known antivirals as positive controls, decoys as negative controls, and rank agreement tests instead of only reporting the top hit. That kind of setup shows you understand assay design, not just docking software. Careful structure prep and transparent filtering also help the work look like research instead of a simple screen.

Project Variations

• Screen FDA-approved antivirals only, then compare how they rank against the full DrugBank library.
• Swap the target from SARS-CoV-2 polymerase to influenza polymerase and test whether the same drug classes rise to the top.
• Add a property filter, such as lipophilicity or polar surface area, to see whether chemistry features predict stable docking hits.

Learn More

• RCSB Protein Data Bank: Search for polymerase structures, bound ligands, and experimental details for your target.
• PubChem: Look up drug structures, synonyms, and basic chemical properties for screening candidates.
• PubMed: Find review articles and recent papers on polymerase inhibitors, docking, and molecular dynamics.
• NCBI Virus: Check viral genome and sequence background for context on the target proteins.
• AutoDock Vina Manual: Read the official docking workflow, output format, and scoring notes.
• OpenMM Documentation: Learn how to set up and analyze molecular dynamics simulations.