Q4: Assignment: Molecular Docking and Virtual Screening. Use sample docking results to rank ligand candidates by binding score, hydrogen bonds, and hydrophobic contacts. Docking scores are hypotheses and require experimental validation.

Molecular Docking and Virtual Screening

Solution and implementation for Q4 from Bioinformatics (bif).

4_docking_virtual_screening.py Download

"""Rank sample ligands by docking score."""

import pandas as pd


data = {
    "Ligand": ["Ligand_A", "Ligand_B", "Ligand_C", "Ligand_D", "Ligand_E", "Ligand_F"],
    "Hydrogen_Bonds": [3, 2, 4, 1, 3, 2],
    "Hydrophobic_Contacts": [5, 4, 6, 3, 7, 5],
    "Molecular_Weight": [310, 285, 340, 250, 360, 300],
    "Binding_Score": [-7.8, -6.4, -8.5, -5.2, -9.1, -7.0],
}

ligands = pd.DataFrame(data)
ranked = ligands.sort_values("Binding_Score")
print(ranked.to_string(index=False))
best = ranked.iloc[0]
print("\nBest candidate:", best["Ligand"])
print("Docking score:", best["Binding_Score"], "kcal/mol")
print("Docking scores are predictions and require experimental validation.")

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Q4: Assignment: Molecular Docking and Virtual Screening. Use sample docking results to rank ligand candidates by binding score, hydrogen bonds, and hydrophobic contacts. Docking scores are hypotheses and require experimental validation.

Molecular Docking and Virtual Screening

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