Model type
Molecular docking procedure; this record is the paper-specific evaluated configuration.
Pipeline
binding-affinity meta-model
This binding-affinity meta-model combines predictions from docking and sequence-based learning models.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Results/Docking Tools (paragraph 4); Results/Meta-models of Docking and DL Tools (paragraph 3)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
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1)At a glance
Biological inputs
Protein/ligand representations and predictions of component affinity models
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods/Dataset Selection/BindingDB (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 7)Outputs
Predicted binding affinity
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Results/Docking Tools (paragraph 4); Methods/GeneralSet Benchmark (paragraph 1)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 |
|---|---|---|
| binding-affinity meta-model: protein-ligand binding affinity prediction Pipeline: binding-affinity meta-modelTask: protein-ligand binding affinity predictionDataset: CASF-2016 Sequence-or-structure meta-model; predicts ln(Kd/Ki) using docked and deep-learning components Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.777 Pearson correlation Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Table 4, Meta-models row, CASF-2016 Benchmark > PCC column Source checking is not independent reproduction. |
How it works
How the evaluated method works
Force-field-derived empirical docking scores and sequence-based deep-learning estimates feed a learned meta-model, optionally with physicochemical or molecular descriptors.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1)What was evaluated
The linked evaluation record identifies binding-affinity meta-model: protein-ligand binding affinity prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-021Strengths and limitations
Strengths and considerations
- Tests whether combining models reduces method-specific prediction errors.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Results/Prediction Synergy and Complementarity of Docking and DL Tools (paragraph 1); Introduction (paragraph 2)
Limitations and conditions
- The exact base-model combination and training database define each meta-model; a generic ensemble name does not identify a reproducible checkpoint.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Discussion (paragraph 2); Results/Prediction Synergy and Complementarity of Docking and DL Tools (paragraph 2)
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-df0efcc0346224Specifications
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 procedure; this record is the paper-specific evaluated configuration.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) |
| Architecture / procedure | Force-field-derived empirical docking scores and sequence-based deep-learning estimates feed a learned meta-model, optionally with physicochemical or molecular descriptors.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) |
| Biological inputs | Protein/ligand representations and predictions of component affinity modelsSourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods/Dataset Selection/BindingDB (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 7) |
| Outputs | Predicted binding affinitySourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Results/Docking Tools (paragraph 4); Methods/GeneralSet Benchmark (paragraph 1) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling; Lee1701/Lee2023a README.md · Methods/Dataset Selection/BindingDB; Methods/Dataset Selection/PDBbind; Methods/Molecular Docking/Implementation of Ligand Docking; Methods/Molecular Docking/Processing of Docking Scores; Methods/Deep Learning/DeepPurpose Library; Methods/Deep Learning/Training of 12 DL Models from the DeepPurpose Library Using BindingDB; Methods/Deep Learning/Training of DL Models Using PDBbind; Methods/Deep Learning/Fine-Tuning of the BDB-Trained Models Using PDBbind; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | binding-affinity meta-model is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | BindingDB version 2020m2 supplies 66,444 filtered ligand–protein complexes for deep-learning model training; the paper explores multiple training databases and model combinations.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods/Dataset Selection/BindingDB (paragraph 1); Methods/Deep Learning/DeepPurpose Library (paragraph 1) |
| 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)Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling; Lee1701/Lee2023a README.md · Methods/Dataset Selection/BindingDB; Methods/Dataset Selection/PDBbind; Methods/Molecular Docking/Implementation of Ligand Docking; Methods/Molecular Docking/Processing of Docking Scores; Methods/Deep Learning/DeepPurpose Library; Methods/Deep Learning/Training of 12 DL Models from the DeepPurpose Library Using BindingDB; Methods/Deep Learning/Training of DL Models Using PDBbind; Methods/Deep Learning/Fine-Tuning of the BDB-Trained Models Using PDBbind; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | The authors provide code and pretrained models at https://github.com/Lee1701/Lee2023a, explicitly describing the release as partial because of a pending patent.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Data and Software Availability |
| Code licence | GNU GPL version 3 (study repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).SourcesLee1701/Lee2023a 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 sourcesSourcesLee1701/Lee2023a 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.
21 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 | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 ["Protein/ligand representations and predictions of component affinity models","binding-affinity meta-model","Predicted binding affinity"] Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 procedure; this record is the paper-specific evaluated configuration. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 Force-field-derived empirical docking scores and sequence-based deep-learning estimates feed a learned meta-model, optionally with physicochemical or molecular descriptors. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 | Lee1701/Lee2023a README.md README.md; checkpoint/access documentation and licence scope Version: 92def517a0edcb0470826353afd81899bcfdb4b1 | 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 Protein/ligand representations and predictions of component affinity models Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Methods/Dataset Selection/BindingDB (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 7) Version: PMC archival version PMC11632770.1 | 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 binding affinity Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Results/Docking Tools (paragraph 4); Methods/GeneralSet Benchmark (paragraph 1) Version: PMC archival version PMC11632770.1 | 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 | Lee1701/Lee2023a README.md Methods/Dataset Selection/BindingDB; Methods/Dataset Selection/PDBbind; Methods/Molecular Docking/Implementation of Ligand Docking; Methods/Molecular Docking/Processing of Docking Scores; Methods/Deep Learning/DeepPurpose Library; Methods/Deep Learning/Training of 12 DL Models from the DeepPurpose Library Using BindingDB; Methods/Deep Learning/Training of DL Models Using PDBbind; Methods/Deep Learning/Fine-Tuning of the BDB-Trained Models Using PDBbind; 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: 92def517a0edcb0470826353afd81899bcfdb4b1 | 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 | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Methods/Dataset Selection/BindingDB; Methods/Dataset Selection/PDBbind; Methods/Molecular Docking/Implementation of Ligand Docking; Methods/Molecular Docking/Processing of Docking Scores; Methods/Deep Learning/DeepPurpose Library; Methods/Deep Learning/Training of 12 DL Models from the DeepPurpose Library Using BindingDB; Methods/Deep Learning/Training of DL Models Using PDBbind; Methods/Deep Learning/Fine-Tuning of the BDB-Trained Models Using PDBbind; 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: PMC archival version PMC11632770.1 | 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
- Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Original source · PMC archival version PMC11632770.1
- Lee1701/Lee2023a README.md · Original source · 92def517a0edcb0470826353afd81899bcfdb4b1
- Lee1701/Lee2023a LICENSE · Original source · 92def517a0edcb0470826353afd81899bcfdb4b1
Technical metadata and extraction receipts
Stable ID: reported-model-df0efcc0346224
- areas
- molecular-interactions
- entity level
- method
- version
- Not reported
- reported name
- binding-affinity meta-model
- 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 record identifies a composed analysis workflow with separately identifiable upstream models, representations or tools and a downstream prediction/scoring procedure. Results belong to that complete composition rather than to an upstream model alone.; source ids: ligand-affinity-meta-model-2024; source locator: Abstract (paragraph 1); Results/Meta-models of Docking and DL Tools (paragraph 1) | Results/Docking Tools (paragraph 4); Results/Meta-models of Docking and DL Tools (paragraph 3); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.