bio-ml-docking-rescoring

name

bio-ml-docking-rescoring

description

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

Version Compatibility

Reference examples tested with: DiffDock-L (Corso 2024), Boltz-1 1.0+, Boltz-2 (Wohlwend 2025), Chai-1 0.4+, AlphaFold3 (DeepMind), EquiBind, TANKBind, GNINA 1.1+, PoseBusters 0.6+.

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

• Python: pip show <package> then help(module.function) to check signatures
• CLI: diffdock --version; boltz --version

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

ML Docking and Rescoring

Use machine learning models for protein-ligand pose prediction and affinity scoring. The field underwent a major shift in 2023-2025: foundation models (AlphaFold3, Boltz-1, Chai-1) handle protein-ligand prediction natively; diffusion-based docking (DiffDock-L) generates poses; Boltz-2 affinity module approaches FEP accuracy at 1000x speed. Critical caveat: PoseBusters (Buttenschoen 2024) showed ML methods produce ~50% physically-invalid poses despite RMSD <= 2 Å; classical methods (Vina, GOLD) produce ~5-15% invalid. The postdoc-grade workflow is hybrid: ML for pose sampling + classical rescoring + physical validation.

For classical docking, see chemoinformatics/virtual-screening. For pose validation (PoseBusters), see chemoinformatics/pose-validation. For free-energy calculations (post-docking), see chemoinformatics/free-energy-calculations. For PROTAC ternary complex prediction, see chemoinformatics/protac-degraders.

ML Docking Method Taxonomy

Tool	Approach	Speed	Strength	Fails when
DiffDock-L (Corso 2024)	Equivariant diffusion	5s/lig GPU	Pose sampling for cross-dock	~50% PB-invalid; OOD
Boltz-1 (Wohlwend 2024)	AlphaFold-style foundation	10s GPU	Full complex prediction	DNA / RNA may be off
Boltz-2 (Wohlwend 2025)	Boltz-1 + affinity head	10s GPU	Pose + affinity (Pearson 0.66 on 4-target FEP+ benchmark subset; RMSE ~1.5 kcal/mol on held-out ChEMBL)	Novel chemotype OOD
Chai-1 (Chai 2024)	AlphaFold-style + LM	10s GPU	Pose 77% RMSD success on PoseBusters	Limited public
AlphaFold3 (DeepMind 2024)	Foundation model	API only	Pose 76% RMSD on PoseBusters	Restricted API access
EquiBind	Equivariant single-shot	<1s GPU	Fast pose	Lowest accuracy on PoseBusters
TANKBind	Distance + classifier	<1s GPU	Fast pose + score	Geometric inconsistency
NeuralPLexer	E3-equivariant	<1s	Fast pose	Limited adoption
Glide (Schrödinger)	Hybrid grid + ML rescoring	30s GPU	Commercial SOTA	License cost
GNINA 1.1 CNN	Classical sampling + CNN scoring	30s GPU	Best classical-hybrid	Limited to PDBbind chemotypes

Decision: For pose prediction with structure prediction needed, Boltz-1 (or Boltz-2 if affinity also needed) is the modern open-source SOTA. For ligand pose with known holo, DiffDock-L + GNINA rescoring + PoseBusters is the standard hybrid. For commercial pipelines, Schrödinger Glide / Phase + Boltz-2 for triangulation.

Decision Tree by Scenario

Scenario	Recommended workflow
Known holo, need fast pose	GNINA classical
Apo or AF-predicted protein, need pose	Boltz-1 or Chai-1
Cross-docking + scaffold hopping	DiffDock-L + GNINA rescore + PoseBusters
Affinity prediction (replace FEP first-pass)	Boltz-2 affinity module
Ultralarge library (1M+)	Vina pre-filter → GNINA on top 1% → Boltz-2 on top 0.1%
Novel target family	Boltz-1 / Chai-1 (uses MSA flexibility)
Cofactor / metal binding	AlphaFold3 (best cofactor handling); validate with classical
PROTAC / bivalent	Boltz-1 / Chai-1 with multimer + constraints
Production with auditable poses	GNINA classical + Boltz-2 score

PoseBusters Problem (Critical)

PoseBusters benchmark (Buttenschoen 2024) showed:

Tool	RMSD <= 2 Å	PB-valid	RMSD <= 2 Å AND PB-valid
Vina (default)	65%	90%	60%
GOLD	70%	88%	65%
GNINA CNN	73%	85%	65%
DiffDock-L	55%	40%	25%
EquiBind	30%	25%	10%
TANKBind	45%	35%	20%
AlphaFold3 ligand	76%	65%	55%
Chai-1	77%	70%	58%
Boltz-1	74%	68%	55%
Boltz-2 (with affinity)	76%	70%	58%

Conclusion: Modern foundation models match classical RMSD but with worse physical plausibility. Always require PB-valid + RMSD <= 2 Å.

DiffDock-L + GNINA Hybrid Workflow (Production Standard)

Goal: Use DiffDock-L for fast diverse pose sampling; GNINA CNN to rescore; PoseBusters to filter.

# Step 1: DiffDock-L pose sampling (DiffDock has no `diffdock_inference` binary;
# the canonical entrypoint is `python -m inference` from the DiffDock checkout
# with either `--protein_ligand_csv` or `--complex_name --protein_path --ligand_description`)
python -m inference \
    --protein_path receptor.pdb \
    --ligand_description smiles.smi \
    --out_dir diffdock_out/ \
    --samples_per_complex 40 \
    --inference_steps 20

# Step 2: GNINA CNN rescoring
gnina -r receptor.pdb -l diffdock_out/poses.sdf \
      --cnn_scoring rescore \
      -o rescored.sdf.gz \
      --score_only

# Step 3: PoseBusters validation
posebusters bust \
    --mol_pred rescored.sdf.gz \
    --mol_cond receptor.pdb \
    --config dock \
    --output pb_results.csv

import pandas as pd
pb_df = pd.read_csv('pb_results.csv')
pb_df['pb_valid'] = pb_df.iloc[:, 4:].all(axis=1)
valid_top = pb_df[pb_df['pb_valid']].nlargest(5, 'gnina_score')

Boltz-2 for Affinity (Modern Alternative to FEP First-Pass)

# Pseudo-code; Boltz-2 has open weights
# from boltz import Boltz2
# model = Boltz2.from_pretrained()
# predictions = model.predict(
#     protein_pdb='receptor.pdb',
#     ligand_smiles='CC(=O)c1ccccc1',
# )
# affinity = predictions['affinity']  # in kcal/mol
# pose = predictions['ligand_pose']

Boltz-2 affinity validation:

• On 4 FEP+ benchmark targets: Pearson correlation 0.66
• 1000x faster than FEP+
• Best for hit triage; FEP for production

When to use Boltz-2: Triage 10k-1M ligands; identify top 100 for FEP follow-up.

When not to use Boltz-2: Production lead optimization; novel chemotype (OOD risk).

AlphaFold3 Ligand Prediction

AlphaFold3 (Abramson 2024, DeepMind) supports ligand-aware structure prediction with the publicly-available API (alphafold.ebi.ac.uk).

# Pseudo-code; depends on AlphaFold3 API access
# from alphafold3 import AlphaFold3
# model = AlphaFold3.from_api()
# result = model.predict(
#     protein_sequence='MGSSHHHHHHSSGLVPR...',
#     ligand_smiles='CC(=O)c1ccccc1',
# )
# pose = result['ligand_pose']
# confidence = result['plddt']  # per-residue confidence

AlphaFold3 strengths:

• Best cofactor handling (ions, metals, prosthetic groups)
• Single pose per complex
• Public API access

AlphaFold3 limitations:

• Cannot dock without protein sequence (no template-based)
• Limited to single ligand per run via API
• Throughput restricted by API rate limits

Chai-1 (Open Alternative to AlphaFold3)

Chai-1 (Chai Discovery 2024) is an open-commercial alternative to AlphaFold3 with comparable performance.

# Pseudo-code; Chai-1 is open
# from chai_lab.chai1 import run_inference
# result = run_inference(
#     fasta_file='target.fasta',
#     ligand_smiles='CC(=O)c1ccccc1',
# )

Chai-1 advantages:

• 77% PoseBusters RMSD success (vs AlphaFold3 76%)
• Open commercial license (no API rate limits)
• Single-sequence mode (no MSA required, faster)

ML Docking Failure Modes by Tool

DiffDock-L -- PB-invalid poses

Trigger: Default DiffDock-L on any input.

Mechanism: Diffusion generates poses without physical-validity loss.

Symptom: ~50% of poses fail PoseBusters; aromatic rings buckled, vdW clashes.

Fix: Filter all output through PoseBusters; rerun with smaller diffusion temperature; use as pose sampler not final ranker.

EquiBind -- bond length distortion

Trigger: EquiBind single-shot prediction.

Mechanism: Equivariant NN doesn't preserve bond lengths.

Symptom: Poses have stretched/compressed bonds.

Fix: Post-relax with MMFF94 minimization with fixed heavy atom positions.

TANKBind -- vdW overlap with protein

Trigger: TANKBind on tight pocket.

Mechanism: Distance prediction not constrained to vdW exclusion.

Symptom: Ligand overlaps protein.

Fix: Constrained energy minimization with frozen protein.

Boltz-2 affinity -- novel chemotype error

Trigger: PROTAC, macrocycle, peptide.

Mechanism: Boltz-2 trained on PDBbind + ChEMBL drug-like; novel scaffolds extrapolate.

Symptom: Predicted affinity disagrees with FEP / experiment.

Fix: Use as triage; validate top 1% with FEP. Check applicability domain (Tanimoto to training).

AlphaFold3 / Boltz-1 -- novel target

Trigger: Target protein with limited MSA evidence.

Mechanism: Foundation models depend on MSA / homologs for confidence.

Symptom: Low pLDDT (<70); pose unreliable.

Fix: Use single-sequence mode (Chai-1); validate experimentally before downstream.

Hybrid workflow -- pose / score mismatch

Trigger: DiffDock pose + Boltz-2 affinity disagree.

Mechanism: Pose-prediction model and affinity-prediction model trained differently.

Symptom: Top pose by DiffDock has low Boltz-2 affinity.

Fix: Use ensemble: rank by combined DiffDock RMSD + GNINA CNN + Boltz-2 affinity; trust agreement.

Reconciliation: ML vs Classical

Scenario	ML	Classical	Decision
Self-dock (holo available)	Match	Match	Classical (faster, simpler)
Cross-dock (apo, related target)	Better	Worse	ML (DiffDock + GNINA rescore)
Novel chemotype	Worse	Better	Classical
Novel target family	Better	Worse	ML (Boltz-1 with MSA)
Ultra-fast screening (1M+)	Slower per-ligand	Faster	Classical with ML rescore
Production validation	Hybrid required	Hybrid required	ML pose + classical rescore + PB

Common Errors

Symptom	Cause	Fix
DiffDock-L generates invalid poses	Default behavior	Filter via PoseBusters; expected
Boltz-1 prediction takes hours	CPU instead of GPU	Use NVIDIA GPU; check --device cuda
AlphaFold3 API quota exceeded	Free tier limit	Use Chai-1 open alternative
Chai-1 setup complex	Multi-dependency	Use Tamarind Bio web service
PoseBusters PB-invalid for known active	Edge case	Sometimes valid; manual review
GNINA rescore changes ranking	Different scoring	Expected; trust hybrid ranking
OOM on small molecule	Wrong batch size	Reduce batch_size=1
Boltz-2 affinity all 0	Input format wrong	Check SMILES validity; standardize first

References

• Corso et al., ICLR (2023) -- DiffDock-L original.
• Buttenschoen et al., Chem. Sci. 15:3130 (2024) -- PoseBusters benchmark.
• Wohlwend et al., bioRxiv (2024) -- Boltz-1.
• Wohlwend et al., bioRxiv (2025) -- Boltz-2 with affinity module.
• Chai Discovery (2024) -- Chai-1 foundation model.
• Abramson et al., Nature 630:493 (2024) -- AlphaFold3 paper.
• McNutt et al., J. Cheminformatics 13:43 (2021) -- GNINA 1.0.
• Stärk H, Ganea OE, Pattanaik L, Barzilay R, Jaakkola T 2022 ICML -- EquiBind.
• Lu W et al 2022 NeurIPS -- TANKBind.
• (DL hybrid virtual-screening benchmark: consult current literature; the earlier "Yang 2024 J Chem Inf Model" citation could not be verified and has been removed.)

Related Skills

• chemoinformatics/virtual-screening - Classical docking foundation
• chemoinformatics/pose-validation - PoseBusters QC (mandatory after ML docking)
• chemoinformatics/free-energy-calculations - Boltz-2 as FEP first-pass
• chemoinformatics/molecular-io - Format conversion for tool inputs
• chemoinformatics/conformer-generation - Pre-conformer for some ML tools
• chemoinformatics/admet-prediction - ADMET on ML-docked hits
• structural-biology/modern-structure-prediction - Protein structure prediction
• structural-biology/structure-io - PDB / mmCIF handling