Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Virtual Screening
Screen chemical libraries against protein targets via molecular docking. Vina is the de-facto default, SMINA adds flexibility (Vinardo scoring, custom scoring), and GNINA adds CNN-based pose scoring (Top-1 redock 58%->73% over Vina, cross-dock 27%->37%). Deep-learning docking (DiffDock-L, EquiBind, NeuralPLexer) competes in pose accuracy, but physical validity is method- and dataset-dependent; the workflow therefore combines ML pose sampling with classical scoring and explicit geometry checks. For ultralarge libraries (>1M), library preparation, hierarchical filtering, and HPC orchestration become the limiting steps.
For pose physical-validity QC, see chemoinformatics/pose-validation. For ML-driven docking + rescoring, see chemoinformatics/ml-docking-rescoring. For covalent docking, see chemoinformatics/covalent-design. For affinity calculations (FEP), see chemoinformatics/free-energy-calculations.
Docking Tool Taxonomy
Tool
Scoring
Speed (sec/lig)
Best at
Fails when
AutoDock Vina 1.2
Vina (empirical)
Hardware- and settings-dependent
Open, well-characterized baseline
Cross-dock; cryptic pockets; metal centers
SMINA
Vina + flexible + custom
Hardware- and settings-dependent
Custom scoring; flexible side chains
Same Vina-scoring caveats
Vinardo
Modified Vina scoring
Hardware- and settings-dependent
Alternative empirical score
Validate on target-relevant controls
GNINA 1.1
CNN or Vina scoring
GPU- and settings-dependent
CNN-assisted pose ranking
Validate transfer to the target and chemotype
AutoDock 4
AD4 + grid maps
Hardware- and settings-dependent
Legacy reference
More setup than Vina
DOCK 6/7
DOCK + Amber
Hardware- and settings-dependent
UCSF DOCK ecosystem
Steep learning curve
Glide (Schrodinger)
GlideScore
License and hardware-dependent
Commercial docking workflow
License cost
GOLD (CCDC)
GOLDScore / ChemScore
License and hardware-dependent
Commercial workflow; metal options
License cost
FlexX (BioSolveIT)
FlexX
License and hardware-dependent
Fragment-based placement
License cost
rDock
rDock
Hardware- and settings-dependent
Open-source alternative
Validate maintenance and target fit
DiffDock-L
Diffusion-generative
GPU- and settings-dependent
Pose sampling for cross-docking
Validate geometry with PoseBusters; see ml-docking-rescoring
EquiBind
Equivariant NN
GPU- and settings-dependent
Single-shot pose generation
Requires independent geometry and ranking checks
Boltz-2 + GNINA rescore
Foundation model + CNN
GPU- and settings-dependent
Experimental multi-model workflow
Benchmark each evidence stream independently
Decision: Use Vina as an open baseline and consider GNINA CNN rescoring when target-relevant redocking or cross-docking controls support it. For large libraries, calibrate a hierarchical Vina -> GNINA -> higher-cost follow-up workflow on measured enrichment, throughput, and retained chemotype diversity.
Decision Tree by Scenario
Scenario
Recommended workflow
Self-dock against known ligand pocket
GNINA gnina --cnn_scoring rescore
Cross-dock to apo or related-target structure
DiffDock-L pose + GNINA rescore + PoseBusters
Ultralarge library (10M+)
Calibrated hierarchical screen: property/alert triage -> Vina -> measured top fraction to GNINA -> higher-cost follow-up
Cryptic pocket / induced fit
Receptor-ensemble docking and, where appropriate, a separately validated complex-prediction model
Allosteric / undefined site
P2Rank for pocket detection -> ensemble dock all pockets
Metal-coordinated ligand
GOLD (commercial) or manually parameterize Vina metal scoring
Covalent inhibitor
See chemoinformatics/covalent-design: DOCKovalent, HCovDock
Fragment screen (<300 Da)
rDock or constrained Vina with seed atoms
Hit-to-lead refinement
Use co-crystal structure if available; MD-relaxed receptor; FEP for affinity
Receptor Preparation
Goal: Convert a protein PDB into a docking-ready format with correct protonation, missing atoms, and removed waters.
Approach: Decide which ligands, cofactors, metals, and structural waters to retain -> fill missing heavy atoms with a structure-repair tool such as PDBFixer -> use PROPKA/PDB2PQR plus manual review to assign pH-dependent protonation -> assign the charge model required by the docking workflow -> prepare receptor PDBQT with a documented AutoDock-compatible tool.
import subprocess
from pathlib import Path
defprepare_receptor(repaired_pdb, pdbqt_out, pH=7.4):
# Decide which waters/cofactors/metals to retain before this function.
base = str(Path(repaired_pdb).with_suffix(''))
pqr_file = f'{base}_pH{pH}.pqr'
subprocess.run(['pdb2pqr', '--ff=AMBER', f'--with-ph={pH}',
repaired_pdb, pqr_file], check=True)
output_basename = str(Path(pdbqt_out).with_suffix(''))
subprocess.run(['mk_prepare_receptor.py', '--read_pqr', pqr_file,
'-o', output_basename, '-p'], check=True)
return pdbqt_out
Common pitfall: Forgetting to add hydrogens at protein pH (7.4) but using pH 7.0 ligand charges. Hist mistakenly protonated. Use PROPKA + manual review of catalytic residues.
Ligand Preparation
Goal: Generate a 3D, docking-ready ligand file from SMILES with appropriate protonation and conformation.
Approach: Supply a documented protomer/tautomer state generated by an appropriate pKa/protomer workflow -> parse it with RDKit -> embed 3D with ETKDGv3 -> minimize with MMFF94 -> write PDBQT with Meeko. MolFromSmiles parses the supplied state and Uncharger neutralizes formal charges; neither predicts protonation at pH 7.4.
from rdkit import Chem
from rdkit.Chem import AllChem
from meeko import MoleculePreparation, PDBQTWriterLegacy
defprepare_ligand(smiles, pdbqt_out):
mol = Chem.MolFromSmiles(smiles)
if mol isNone:
raise ValueError(f'invalid SMILES: {smiles}')
mol = Chem.AddHs(mol)
embed_status = AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())
if embed_status != 0:
raise RuntimeError('ETKDGv3 failed to generate a ligand conformer')
ifnot AllChem.MMFFHasAllMoleculeParams(mol):
raise ValueError('MMFF94 parameters are unavailable for this ligand')
optimization_status = AllChem.MMFFOptimizeMolecule(mol)
if optimization_status != 0:
raise RuntimeError('MMFF94 ligand optimization did not converge')
# meeko 0.5+ API: prepare() returns a list of MoleculeSetup objects;# use PDBQTWriterLegacy.write_string() to materialize the PDBQT block.
mk_prep = MoleculePreparation()
setups = mk_prep.prepare(mol)
pdbqt_text, is_ok, err = PDBQTWriterLegacy.write_string(setups[0])
ifnot is_ok:
raise RuntimeError(f'meeko PDBQT export failed: {err}')
withopen(pdbqt_out, 'w') as f:
f.write(pdbqt_text)
return pdbqt_out
meeko (AutoDock developers' tool) handles torsion tree creation, rotamer flagging, and PDBQT writing -- preferred over Open Babel's PDBQT writer. Note: meeko 0.5+ separated the writer (PDBQTWriterLegacy) from MoleculePreparation; older code using prep.write_pdbqt_file() is deprecated.
Binding Site Detection
When the binding pocket is not known (apo target, novel allosteric site):
Tool
Approach
Output
P2Rank (Krivak 2018)
ML on protein surface descriptors
Ranked pocket list with center coords
fpocket (Le Guilloux 2009)
Voronoi tessellation
Pocket descriptor list
DoGSiteScorer
Geometric + drugability
Pocket list with score
AutoSite (Vina)
Affinity map clustering
Pocket centers
AlphaFill
Transplant ligands/cofactors from homologous experimental structures into AlphaFold models
Plausible binding-site components for review
prank predict -f receptor.pdb -o pockets/
P2Rank output <receptor>_predictions.csv lists pocket centers with scores. The highest model score does not identify a pocket as orthosteric or biologically relevant; verify ranked pockets against co-crystal, mutagenesis, SAR, or other structural evidence.
Vina Docking (Single Ligand)
# AutoDock Vina Python API requires Vina 1.2+; for Vina 1.1 use subprocess CLI:# subprocess.run(['vina', '--receptor', ..., '--ligand', ..., '--center_x', ...], check=True)from vina import Vina
defdock_single(receptor_pdbqt, ligand_pdbqt, center, box_size,
exhaustiveness=8, n_poses=10):
v = Vina(sf_name='vina')
v.set_receptor(receptor_pdbqt)
v.set_ligand_from_file(ligand_pdbqt)
v.compute_vina_maps(center=center, box_size=box_size)
v.dock(exhaustiveness=exhaustiveness, n_poses=n_poses)
return v.energies(), v.poses()
Exhaustiveness:8 is the Vina default. Increasing it increases search effort, but runtime and pose recovery depend on hardware, ligand flexibility, box size, and software version. Benchmark settings such as 8, 16, 32, and 64 on target-relevant controls instead of assigning universal timing or quality labels.
Vina's rmsd_lb and rmsd_ub are lower and upper heavy-atom RMSD bounds between a reported mode and the best-scoring mode; the bounds differ in how symmetry-equivalent atoms are handled. They are not pose-versus-experimental-reference RMSDs. Use an external symmetry-aware RMSD to a reference pose for accuracy QC.
none: no CNN; use the selected empirical scoring function throughout
rescore (default): use empirical scoring during the search, then CNN-rerank the final poses; least computationally expensive CNN option
refinement: use the CNN to refine poses after Monte Carlo chains and to rank the final poses; approximately 10 times slower than rescore on a GPU in the official documentation
metrorescore: use CNN scoring in the Metropolis search and rescore the resulting poses
metrorefine: use CNN scoring in the Metropolis search and refine the resulting poses
all: use the CNN scoring function throughout; the official documentation describes this as extremely computationally intensive and not recommended
The six choices above are from GNINA 1.3. Earlier releases expose a smaller set; check gnina --help for the installed executable rather than assuming every mode is available.
Critical: GNINA distributions include multiple named CNN models/ensembles rather than one universally described "PDBbind 2019" model. Record the selected model or ensemble and validate it with known co-crystal redocking and, when relevant, cross-docking controls.
Virtual Screening Pipeline (Hierarchical)
Goal: Screen 10M-compound library down to top-1k candidates for follow-up.
Approach: Three-stage filter. The 1% and top-1000 selections below are repository starting heuristics; choose production cutoffs from target-relevant enrichment, diversity, and throughput measurements.
Pseudo-code skeleton (orchestrator). Each helper function delegates to a dedicated skill: drug-likeness filter to chemoinformatics/admet-prediction, single-ligand Vina/GNINA to dock_single defined earlier in this skill, PoseBusters QC to chemoinformatics/pose-validation.
import pandas as pd
from concurrent.futures import ProcessPoolExecutor
from functools import partial
# Stub helpers to be implemented per project; see the cross-referenced skills.defdrug_like_filter(df):
raise NotImplementedError('Implement via chemoinformatics/admet-prediction (Lipinski+Veber+PAINS)')
defvina_dock(smi, receptor_pdbqt, center, box):
raise NotImplementedError('Wrap dock_single() above; return best affinity')
defgnina_rescore(smi, receptor_pdbqt, center, box):
raise NotImplementedError('Wrap gnina --cnn_scoring rescore subprocess call')
defpose_validate(df):
raise NotImplementedError('Implement via chemoinformatics/pose-validation (PoseBusters)')
defvs_pipeline(library_smi, receptor_pdbqt, center, box, output_dir, n_workers=16):
df = pd.read_csv(library_smi)
df_stage1 = drug_like_filter(df)
worker = partial(vina_dock, receptor_pdbqt=receptor_pdbqt,
center=center, box=box)
with ProcessPoolExecutor(max_workers=n_workers) as ex:
affinities = list(ex.map(worker, df_stage1['smiles']))
df_stage1['vina_affinity'] = affinities
df_stage2 = df_stage1.nsmallest(int(len(df_stage1) * 0.01), 'vina_affinity')
df_stage2['gnina_affinity'] = df_stage2['smiles'].apply(
lambda smi: gnina_rescore(smi, receptor_pdbqt, center, box))
df_stage3 = df_stage2.nsmallest(1000, 'gnina_affinity')
return pose_validate(df_stage3)
For very large libraries, use a restartable scheduler-backed workflow and measure throughput on a representative tranche. Record hardware, software version, box dimensions, ligand flexibility, and failure rate with every throughput estimate.
Library sizes and availability change frequently. Obtain counts from the provider or versioned database at execution time rather than copying a static total into a workflow.
For ultralarge VS, the following percentages and thresholds are repository starting heuristics that must be calibrated for the target and library:
Apply a documented property/alert policy while retaining flagged and rejected counts
If known actives exist, test a permissive 2D-similarity prefilter such as ECFP4 Tanimoto >=0.4 and measure active/chemotype retention
Vina dock the filtered subset
Rescore top 1% with GNINA
Rescore top 0.1% with MM/GBSA or FEP
Lyu et al. (2019) screened 170 million make-on-demand compounds against AmpC and the D4 dopamine receptor. Of 549 D4 candidates synthesized and tested, 81 were new active chemotypes and 30 had submicromolar activity.
Per-Tool Failure Modes
Vina -- cross-dock failure
Trigger: Receptor structure not the holo (co-crystal with ligand from another binder).
Mechanism: Cross-docking introduces receptor-conformation mismatch, so pose recovery can be substantially worse than self-docking; the size of the decrease is benchmark- and target-dependent.
Symptom: Top-ranked pose makes no geometric sense; key contacts missing.
Fix: GNINA CNN scoring or ensemble docking. For genuine apo, predict holo with AlphaFold3 / Boltz-1 then dock.
GNINA CNN -- novel chemotype out-of-distribution
Trigger: Ligand chemotype not in PDBbind training.
Symptom: Affinity prediction far worse than Vina alone.
Fix: Use --cnn_scoring rescore (sampling still by Vina) rather than CNN sampling. Validate against co-crystal of close analog.
Box too small
Trigger: Binding box defined tightly around small ligand reference.
Mechanism: Vina explores only within the box; large analogs cannot fit.
Symptom: Many ligands report "no valid pose"; chemotype-biased hits.
Fix: Derive the box from the reference ligand or known pocket and add enough explicit padding for the largest intended ligands to translate and rotate. Then verify containment and redocking/search convergence on controls. There is no universal padding value or 25 A cube that fits every ligand series.
Multi-pocket protein -- wrong site
Trigger: Protein has multiple binding sites (orthosteric + allosteric).
Mechanism: P2Rank or AutoBox picks the most "drugable" pocket; not always the desired one.
Symptom: Hits dock in wrong pocket; SAR confusing.
Fix: Verify pocket from co-crystal data; explicitly set center_x/y/z from known ligand centroid.
DiffDock-L -- PoseBusters invalid
Trigger: Default DiffDock-L output for any receptor.
Mechanism: Diffusion-generated poses are not guaranteed to satisfy every bond-geometry, stereochemistry, and intermolecular-clash check; failure rates vary by method and benchmark.
Symptom: Poses look reasonable but fail PoseBusters checks.
Fix: Filter to PB-valid (PoseBusters); rescore with GNINA. See chemoinformatics/pose-validation.
Wrong ionization state
Trigger: Ligand or receptor residues protonated incorrectly at pH 7.4.
Mechanism: Aspartate/glutamate/histidine protonation depends on local environment; default protonation may be wrong.
Symptom: Salt bridges missing; poses misranked.
Fix: Run PROPKA on the receptor to estimate residue pKas; for catalytic histidines, manually inspect protonation and tautomer state in the local environment.
Reconciliation: Vina vs GNINA Disagreement
Vina top pose
GNINA top pose
Action
Same pose, similar score
Same pose, similar score
Treat agreement as supporting evidence; still run physical-validity checks
Vina top pose ≠ GNINA top pose
Same pocket, different orientation
Retain both and compare against target-relevant controls or interaction evidence
Vina excellent, GNINA mediocre
Different pose, very different score
Inspect both poses; do not infer which method is correct from score disagreement alone
Both poor scores
Many ligands score similarly poor
Wrong pocket / protein conformation; reconsider receptor
Common Errors
Symptom
Cause
Fix
Vina segfault
PDBQT corrupted (atom names)
Re-prep with meeko
GNINA hangs
GPU OOM
Reduce concurrent work and, if fewer output poses are acceptable, use --num_modes 5
All affinities very poor (-3 to -5)
Wrong protonation; ligand too large for box
Re-check pKa; expand box
Identical affinity across ligands
Receptor grid not computed
Call v.compute_vina_maps() before dock
Pose poses make no sense
Receptor and ligand in different frames
Ensure same coordinate origin
Metal-coordination pose is wrong
The selected scoring/preparation protocol lacks a validated model for that metal geometry
Use a metal-specific validated workflow; the Vina executable can use AutoDock4Zn maps with --scoring ad4 for zinc, while other metals require separately supported parameters/protocols
