fbdd

Use for fragment-based drug design (FBDD): Rule of 3 filtering, ligand efficiency metrics (LE/LLE/BEI/LELP), fragment library design, fragment docking (Vina/Gnina), fragment growing/linking/merging strategies, and Abad-Zapatero efficiency plots.

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

FileContent
references/fbdd-theory.mdFragment rules (Rule of 3), LE/LLE/LLEAT/BEI/SEI, Hann complexity model, fragment-to-lead strategies (grow/link/merge), success stories
references/fragment-library.mdLibrary design: RDKit filters (Ro3/PAINS/flatness/rigidity), 3D sp3 character, commercial sources, diversity selection, quality checks
references/fragment-docking.mdLow-MW docking pitfalls, Vina fragment settings, ROCS shape screening, Smina fragment mode, pose clustering, hotspot validation
references/fragment-growing.mdScaffold growing (R-group enumeration, MMPA vectors), fragment merging (MCS-based), FBDD-aware REINVENT, SynthesizabilityOracle, elaboration scoring
references/efficiency-metrics.mdLE/LLE/LLEAT/BEI/SEI formulas, efficiency evolution plots, Abad-Zapatero plots, LELP, GE (group efficiency), metric-driven SAR

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

PropertyFragment (Ro3)Lead-likeDrug-like (Ro5)
MW≤ 300 Da≤ 400 Da≤ 500 Da
cLogP≤ 3≤ 4≤ 5
HBD≤ 3≤ 4≤ 5
HBA≤ 3≤ 8≤ 10
PSA—≤ 120 Ų—
Rotatable bonds≤ 3≤ 7≤ 10

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