Model type
Molecular docking model; this record is the paper-specific evaluated configuration.
Configuration
DiffDock
This configuration predicts ligand poses for the SARS-CoV-2 and MERS-CoV main proteases in the ASAP challenge setting.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Conclusions (paragraph 1); Introduction (paragraph 2)How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1)At a glance
Biological inputs
Main-protease protein information and ligand structures/SMILES
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1)Outputs
Predicted ligand binding poses
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina (paragraph 2); Methodologies/Potency Prediction/LRIP-SF/Pose Generation (paragraph 2)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| DiffDock: Ligand potency prediction using generated poses Configuration: DiffDockTask: Ligand potency prediction using generated posesDataset: SARS-CoV-2 Mpro ligands Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score. Independent external evaluation · Evaluation metadata: needs review | ||
| 0.695 Pearson R Unit: unitless · Direction: unknown | Uncertainty: ± 0.037 Scored: Not reported · Eligible: Not reported | source checkedA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Table 3, DiffDock row, Pearson’s R column Source checking is not independent reproduction. |
How it works
How the evaluated method works
The receptor PDB and ligand SMILES enter the official default DiffDock configuration. Multiple poses are generated, internally confidence-ranked and the top-scoring pose is selected.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1)Underlying method and version boundaries
DiffDock is a molecular-docking implementation that produces ligand poses and confidence estimates. Its confidence values and predicted coordinates are different outputs from an experimentally calibrated binding-affinity measurement.
Sources
gcorso/DiffDock README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies DiffDock: Ligand potency prediction using generated poses. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-048Strengths and limitations
Strengths and considerations
- Evaluates pose generation and downstream affinity estimation separately.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Conclusions (paragraph 3); Discussion (paragraph 10)
Limitations and conditions
- An affinity result obtained by LRIP-SF on a predicted pose must not be assigned to the pose generator alone.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Potency Prediction/LRIP-SF/Pose Generation (paragraph 1); Results/Potency Prediction (paragraph 3)
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-415ee22f46526cSpecifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Molecular docking model; this record is the paper-specific evaluated configuration.Sourcesgcorso/DiffDock README.md · README.md model description |
| Architecture / procedure | The receptor PDB and ligand SMILES enter the official default DiffDock configuration. Multiple poses are generated, internally confidence-ranked and the top-scoring pose is selected.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) |
| Biological inputs | Main-protease protein information and ligand structures/SMILESSourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) |
| Outputs | Predicted ligand binding posesSourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina (paragraph 2); Methodologies/Potency Prediction/LRIP-SF/Pose Generation (paragraph 2) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases; gcorso/DiffDock README.md · Methodologies/Pose Prediction/Data Preparation; Methodologies/Pose Prediction/Molecular Docking with Glide; Methodologies/Pose Prediction/Molecular Docking with AutoDock Vina; Methodologies/Pose Prediction/Flexible Ligand Superposition with FlexS; Methodologies/Pose Prediction/Deep Learning-Based Modeling with AlphaFold3; Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | DiffDock is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | The study contains 770 SARS-CoV-2 training complexes and test sets of 98 SARS-CoV-2 and 97 MERS-CoV complexes; these are study partitions, not necessarily upstream-model training corpora.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methodologies/Pose Prediction/Data Preparation (paragraph 2); Results/Potency Prediction (paragraph 5) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases; gcorso/DiffDock README.md · Methodologies/Pose Prediction/Data Preparation; Methodologies/Pose Prediction/Molecular Docking with Glide; Methodologies/Pose Prediction/Molecular Docking with AutoDock Vina; Methodologies/Pose Prediction/Flexible Ligand Superposition with FlexS; Methodologies/Pose Prediction/Deep Learning-Based Modeling with AlphaFold3; Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official upstream implementation and usage documentation: https://github.com/gcorso/DiffDock/blob/85c49b60d3e0b0182a59ee43a34a6d7036981284/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesgcorso/DiffDock README.md · README.md; installation, model download and usage instructions |
| Code licence | MIT (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).Sourcesgcorso/DiffDock LICENSE · LICENSE; complete licence text |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcesgcorso/DiffDock README.md · README.md; checkpoint/access documentation and licence scope |
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
22 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Main-protease protein information and ligand structures/SMILES","DiffDock","Predicted ligand binding poses"] Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Molecular docking model; this record is the paper-specific evaluated configuration. Individual claims | gcorso/DiffDock README.md README.md model description Version: 85c49b60d3e0b0182a59ee43a34a6d7036981284 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure The receptor PDB and ligand SMILES enter the official default DiffDock configuration. Multiple poses are generated, internally confidence-ranked and the top-scoring pose is selected. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | gcorso/DiffDock README.md README.md; checkpoint/access documentation and licence scope Version: 85c49b60d3e0b0182a59ee43a34a6d7036981284 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Main-protease protein information and ligand structures/SMILES Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Predicted ligand binding poses Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina (paragraph 2); Methodologies/Potency Prediction/LRIP-SF/Pose Generation (paragraph 2) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | gcorso/DiffDock README.md Methodologies/Pose Prediction/Data Preparation; Methodologies/Pose Prediction/Molecular Docking with Glide; Methodologies/Pose Prediction/Molecular Docking with AutoDock Vina; Methodologies/Pose Prediction/Flexible Ligand Superposition with FlexS; Methodologies/Pose Prediction/Deep Learning-Based Modeling with AlphaFold3; Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 85c49b60d3e0b0182a59ee43a34a6d7036981284 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methodologies/Pose Prediction/Data Preparation; Methodologies/Pose Prediction/Molecular Docking with Glide; Methodologies/Pose Prediction/Molecular Docking with AutoDock Vina; Methodologies/Pose Prediction/Flexible Ligand Superposition with FlexS; Methodologies/Pose Prediction/Deep Learning-Based Modeling with AlphaFold3; Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2; Methodologies/Pose Prediction/Deep Learning-Based Modeling with Gnina; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: version of record | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
3 source records and release history
- A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Original source · version of record
- gcorso/DiffDock README.md · Original source · 85c49b60d3e0b0182a59ee43a34a6d7036981284
- gcorso/DiffDock LICENSE · Original source · 85c49b60d3e0b0182a59ee43a34a6d7036981284
Technical metadata and extraction receipts
Stable ID: reported-model-415ee22f46526c
- areas
- molecular-interactions
- entity level
- method
- version
- Not reported
- reported name
- DiffDock
- historical missing metadata
- version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: mpro-pose-affinity-2025; evidence-reported-base-diffdock-readme-md; source locator: Methodologies/Pose Prediction/Deep Learning-Based Modeling with DiffDock (paragraph 1); Methodologies/Pose Prediction/Deep Learning-Based Modeling with Boltz-2 (paragraph 1) | README.md model description | Conclusions (paragraph 1); Introduction (paragraph 2); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.