Fragment-Based Drug Design (FBDD)

Purpose

Design and analyze fragment libraries, compute ligand efficiency metrics, perform fragment docking, and execute fragment-to-lead elaboration (growing, linking, merging) with computational support.

When to Use This Skill

• Building or filtering a fragment library
• Computing LE/LLE/LLEAT efficiency metrics
• Docking fragments into a target (weak binding, requires special settings)
• Growing a fragment hit toward lead-like compounds
• Merging two fragment hits sharing a common substructure
• Analyzing X-ray fragment screening data

Reference Files

Quick Routing

"Build a fragment library" → fragment-library.md

"Dock fragments into my target" → fragment-docking.md

"I have a fragment hit, want to grow it" → fragment-growing.md

"Track efficiency as I optimize" → efficiency-metrics.md

"What makes a good fragment?" → fbdd-theory.md

Core Concept: Rule of 3

Minimal LE Calculation

def ligand_efficiency(pIC50, n_heavy_atoms):
    """LE = ΔG / HAC ≈ 1.37 * pIC50 / HAC (kcal/mol per heavy atom)"""
    return 1.37 * pIC50 / n_heavy_atoms

# Good fragment: LE ≥ 0.3 kcal/mol/HA
# Drug-like optimum: LE ≥ 0.3 (maintain or improve during optimization)

Integration with ALKYL Skills

• Fragment docking: docking skill (Vina/Gnina, lower exhaustiveness ok)
• Fragment diversity: chem_diversity.py (MaxMin)
• Fragment filtering: chem_filter.py, chem_batch.py
• Growing enumeration: chem_react.py (ReactionFromSmarts)
• MMPA elaboration: mmpa skill
• Generative growing: generative-design skill (REINVENT with fragment constraint)
• 3D visualization: py3Dmol skill