Version Compatibility

Reference examples tested with: PoseBusters 0.6+, RDKit 2024.09+, pandas 2.2+, posecheck 0.5+ (optional).

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Pose Validation

Test docked or AI-generated protein-ligand poses for physical plausibility. PoseBusters (Buttenschoen 2024) is the modern gold standard: a suite of geometric, chemical, and energetic checks that flag implausible poses (planar aromatic rings now non-planar, vdW clashes, broken bonds, wrong chirality, unrealistic torsions). The PoseBusters benchmark showed that AI-based docking methods (DiffDock, EquiBind, TANKBind) produce ~50% physically-invalid poses despite reporting good RMSD; classical methods (Vina, GOLD) produce ~5-15% invalid. PB-valid status is therefore essential for downstream SAR, FEP setup, or generative model training.

For docking, see chemoinformatics/virtual-screening. For ML docking specifically, see chemoinformatics/ml-docking-rescoring.

PoseBusters Test Suite

PoseBusters runs ~20 individual checks grouped into:

Check group	What it tests	Threshold
Sanity	Ligand chemical sanity	RDKit sanitization passes
Bond lengths	Bond lengths within reference	< 2 std from RDKit defaults
Bond angles	Bond angles within reference	< 2 std from RDKit defaults
Internal steric	No intra-ligand vdW clash	vdW overlap < 1.0 Å
Aromatic ring planarity	Aromatic rings planar	< 0.25 Å RMS deviation
Double-bond stereo	Z/E preserved	Match input SMILES
Internal energy	Strain not absurd	UFF energy < 100 kcal/mol typical
Volume overlap	vdW overlap with protein	< 7.5% of ligand vdW volume
Distance to protein	Not floating in solvent	Closest contact < 5 Å
Chirality	R/S preserved from input	Match input SMILES

A pose passing ALL tests is "PB-valid". Combined PB-valid + RMSD <= 2 Å is the modern criterion.

When to Apply PoseBusters

Workflow	PoseBusters use	Action
Self-docking (validating method)	Required	Compare PB-valid + RMSD <= 2A
Cross-docking	Required	PB-valid + RMSD <= 2A; account for protein flexibility
Virtual screening top hits	Required	Filter to PB-valid before MM/GBSA / FEP
AI docking (DiffDock, etc.)	Mandatory	Often 50% fail; otherwise can't compare to classical
Generated structures (RFdiffusion)	Required	Generation often produces clashes
Boltz-2 / AlphaFold3 ligand poses	Recommended	Same family of issues; less frequent than DiffDock
Production FEP setup	Required	Strain in input pose breaks FEP convergence

PoseBusters Usage

from posebusters import PoseBusters

bust = PoseBusters(config='redock')

results = bust.bust(
    mol_pred='predicted.sdf',
    mol_true='reference.sdf',
    mol_cond='receptor.pdb',
)

config options and included checks:

Config	Includes	When to use
redock	All checks + RMSD vs reference + protein vdW overlap	Self-docking benchmarks, retrospective validation
dock	All checks except RMSD reference	Blind docking, prospective virtual screening
mol	Intra-ligand only (sanity, bonds, angles, rings, stereo, energy)	Conformer QC; no protein context

Output DataFrame columns include: mol_pred_loaded, sanitization, all_atoms_connected, bond_lengths, bond_angles, internal_steric_clash, aromatic_ring_flatness, double_bond_flatness, internal_energy, protein_flexibility, minimum_distance_to_protein, minimum_distance_to_organic_cofactors, minimum_distance_to_inorganic_cofactors, volume_overlap_with_protein, volume_overlap_with_organic_cofactors, volume_overlap_with_inorganic_cofactors. All bool; True = pass.

Output: DataFrame with one column per check, one row per pose, boolean pass/fail.

Python Library API

Goal: Programmatically validate a docked-pose SDF against a receptor PDB and produce a PB-valid filter.

Approach: Instantiate PoseBusters(config='dock'), call bust() on the SDF + PDB pair, and AND-aggregate all boolean check columns into a single pb_valid flag.

from posebusters import PoseBusters
import pandas as pd

bust = PoseBusters(config='dock')

results = bust.bust(
    mol_pred='/path/to/docked_poses.sdf',
    mol_cond='/path/to/receptor.pdb',
)

results['pb_valid'] = results.iloc[:, 4:].all(axis=1)
valid = results[results['pb_valid']]
print(f'{len(valid)} / {len(results)} poses are PB-valid')

Strain Energy Quantification

Beyond binary PB-valid, quantitative strain energy distinguishes "marginal" from "egregious" poses.

Goal: Quantify how far each docked pose is from its lowest-energy free conformer in MMFF94 energy units.

Approach: Generate a reference conformer ensemble (ETKDGv3 + MMFF94), take the global minimum energy as baseline, and report docked_energy - min_ref_energy as strain.

from rdkit import Chem
from rdkit.Chem import AllChem

def ligand_strain(docked_sdf, n_ref=20):
    suppl = Chem.SDMolSupplier(docked_sdf, removeHs=False)
    strains = []
    for docked in suppl:
        if docked is None:
            continue

        smi = Chem.MolToSmiles(docked)
        ref = Chem.MolFromSmiles(smi)
        ref = Chem.AddHs(ref)
        AllChem.EmbedMultipleConfs(ref, numConfs=n_ref, params=AllChem.ETKDGv3())
        AllChem.MMFFOptimizeMoleculeConfs(ref)

        ref_energies = []
        for c in range(ref.GetNumConformers()):
            ff = AllChem.MMFFGetMoleculeForceField(
                ref,
                AllChem.MMFFGetMoleculeProperties(ref),
                confId=c
            )
            ref_energies.append(ff.CalcEnergy())
        min_ref = min(ref_energies)

        docked_ff = AllChem.MMFFGetMoleculeForceField(
            docked,
            AllChem.MMFFGetMoleculeProperties(docked)
        )
        docked_e = docked_ff.CalcEnergy() if docked_ff else None

        strains.append({
            'min_ref_energy': min_ref,
            'docked_energy': docked_e,
            'strain': docked_e - min_ref if docked_e else None,
        })
    return strains

Strain interpretation:

• < 2 kcal/mol: ideal; close to relaxed
• 2-5 kcal/mol: realistic for bioactive conformer
• 5-10 kcal/mol: marginal; check for plausibility

• 10 kcal/mol: implausible without exceptional binding

(Boström 1998 showed bioactive ligands have median strain ~2-4 kcal/mol.)

vdW Overlap with Protein

PoseBusters limit: protein-ligand vdW overlap < 7.5% of ligand vdW volume. Computed via:

def vdw_overlap_fraction(ligand_pdb, protein_pdb):
    from rdkit import Chem
    from rdkit.Chem import AllChem
    # Use RDKit AllChem.UFFGetMoleculeForceField for vdW radii
    # Compute pairwise distances, sum overlap volume
    # (full implementation in PoseBusters; this is a sketch)
    return overlap_fraction

In practice, PoseBusters' bust(...) does this automatically.

Aromatic Ring Planarity

import numpy as np

def aromatic_planarity(mol):
    deviations = []
    for ring in mol.GetRingInfo().AtomRings():
        ring_atoms = [mol.GetAtomWithIdx(i) for i in ring]
        if not all(a.GetIsAromatic() for a in ring_atoms):
            continue
        coords = np.array([mol.GetConformer().GetAtomPosition(i)
                          for i in ring])
        centroid = coords.mean(axis=0)
        centered = coords - centroid
        _, s, vh = np.linalg.svd(centered)
        normal = vh[-1]
        deviation = np.abs(centered @ normal).max()
        deviations.append(deviation)
    return max(deviations) if deviations else 0

Aromatic ring deviation > 0.25 Å is implausible; flag.

Per-Tool Failure Modes

DiffDock-L -- chirality inversion

Trigger: Default DiffDock-L on chiral ligand.

Mechanism: Diffusion sampling can invert stereocenters; not constrained by training.

Symptom: PoseBusters chirality check fails; SMILES from pose != input SMILES.

Fix: Filter to PB-valid; rerun with constrained stereo if supported.

EquiBind -- planar aromatic violation

Trigger: EquiBind single-shot pose prediction.

Mechanism: Equivariant NN does not preserve ring planarity.

Symptom: Aromatic rings buckled.

Fix: Post-relax with UFF/MMFF94 minimization before downstream use.

TANKBind -- vdW clash

Trigger: TANKBind pose in tight pocket.

Mechanism: Distance prediction not constrained to vdW radii.

Symptom: PoseBusters vdW overlap fails.

Fix: Post-process with constrained energy minimization (UFF + frozen heavy atoms 10% relaxation).

Boltz-1 -- bond length distortion

Trigger: Cofactor-bound complex.

Mechanism: Model trained on bond-length distribution; uncommon ligand pushes outside training distribution.

Symptom: Bond lengths >2 std from RDKit reference.

Fix: Post-relax with MMFF94 (constrained to maintain pose).

High strain after Vina docking

Trigger: Highly constrained pocket; flexible ligand.

Mechanism: Vina exhaustiveness limits sampling; not enough rotation search.

Symptom: Strain energy > 8 kcal/mol but pose passes PoseBusters geometric.

Fix: Re-dock with exhaustiveness=32; or post-relax pose with UFF inside pocket.

Reconciliation: PoseBusters vs RMSD

RMSD <= 2A	PB-valid	Action
Yes	Yes	High confidence; use for SAR
Yes	No	RMSD-good but unphysical; re-relax
No	Yes	Physical but wrong binding mode; consider ensemble
No	No	Both wrong; reject

In DiffDock benchmarks, ~25% of poses are RMSD <= 2A AND PB-valid; ~25% RMSD <= 2A but NOT PB-valid (the "false success" rate of AI methods).

Integration into VS Pipeline

import pandas as pd
from posebusters import PoseBusters

def pose_qc_pipeline(docked_sdfs, receptor_pdb):
    bust = PoseBusters(config='dock')
    all_results = []
    for sdf in docked_sdfs:
        r = bust.bust(mol_pred=sdf, mol_cond=receptor_pdb)
        r['pb_valid'] = r.iloc[:, 4:].all(axis=1)
        r['source'] = sdf
        all_results.append(r)
    df = pd.concat(all_results)

    df['rank'] = df.groupby('source')['pb_valid'].cumsum()
    valid_top = df[df['pb_valid']].groupby('source').head(1)
    return valid_top

Common Errors

Symptom	Cause	Fix
PoseBusters reports None	Sanitize failed on input pose	Standardize before docking; check SDF format
RMSD not computed	No reference provided	Pass mol_true parameter
All checks pass for invalid pose	Wrong receptor file format	Use PDB with hydrogens; PDBQT may not work
vdW overlap false positive on covalent	Covalent bond counted as clash	Use covalent docking-specific validation
Strain calculation slow	Too many reference conformers	Reduce n_ref to 5-10
Posebusters config error	Wrong config name	Options: redock, dock, mol
posecheck unavailable	Different tool, similar purpose	pip install posecheck for alternative

References

• Buttenschoen et al., Chem. Sci. 15:3130 (2024) -- PoseBusters benchmark and tool.
• Boström, J. Comput.-Aided Mol. Des. 12:383 (1998) -- bioactive ligand strain.
• Hawkins, J. Chem. Inf. Model. 57:1747 (2017) -- conformer ensemble best practices.
• Krasoulis et al., J. Cheminformatics 14:24 (2022) -- DENVIS pose quality assessment.
• Cole et al., Acta Crystallogr. D 64:144 (2008) -- ligand strain in PDB structures.

Related Skills

• chemoinformatics/virtual-screening - Source of poses to validate
• chemoinformatics/ml-docking-rescoring - DiffDock, EquiBind, TANKBind validation
• chemoinformatics/molecular-io - SDF format handling
• chemoinformatics/conformer-generation - Generate reference conformer ensemble for strain
• chemoinformatics/free-energy-calculations - PoseBusters-valid poses for FEP input
• chemoinformatics/covalent-design - Covalent pose validation