Datasets
PDBbind v2020 refined/core collections and BindingDB-trained component models.
Task
protein-ligand binding affinity prediction
Affinity prediction combines refined-set training with core-set and separately filtered general-set evaluations.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results tableOverview
Metrics
RMSE and Pearson correlation coefficient for binding-affinity evaluation; target-specific ranking collections are reported separately.
Allowed inputs
Protein–ligand representations used by component scoring models.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results tableEvaluation procedure diagram
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results tablelimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
1 evaluation · 1 result. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Pipeline: binding-affinity meta-model | Task: protein-ligand binding affinity prediction Dataset: CASF-2016 | 0.777 Pearson correlation unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourcebinding-affinity meta-model: protein-ligand binding affinity prediction Sequence-or-structure meta-model; predicts ln(Kd/Ki) using docked and deep-learning components Aggregation: Not reported Improved 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. Release 2026-09-29-06401fd5b220.
Methods and evaluation design
Procedure, tasks and evaluated configurations
How it works
Evaluation methodology
PDBbind v2020 refined/core collections and BindingDB-trained component models. CASF2016 core complexes are excluded from refined-set training; a filtered general-set subset excludes refined/core entries. RMSE and Pearson correlation coefficient for binding-affinity evaluation; target-specific ranking collections are reported separately. Table 4 compares the docking and sequence-model components with HAC-Net, FAST’s pretrained SG-CNN and KDeep. Training sets and affinity units differ: HAC-Net is retrained, SG-CNN retains its original training, and KDeep uses limited retraining or its server default. PerSpect ML is discussed from published results rather than rerun. PDBbind fine-tuning excludes the CoreSet, and the filtered GeneralSet excludes both RefinedSet and CoreSet. The separate three-target ranking test additionally enforces less than 30% protein similarity. These distinct controls do not establish a BindingDB-overlap audit for the CASF-2016 or GeneralSet rows.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table; Methods: Comparison to Other Tools; Results: Comparison with Structure-Based Tools; Table 4; Methods: Fine-Tuning of the BDB-Trained Models Using PDBbind; GeneralSet Benchmark; Ranking BenchmarkRecorded evaluations
Each evaluation records what was tested and under which conditions.
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Limitations and conditions
- Failed docking predictions are excluded from benchmark subsets, changing test coverage. BindingDB-trained and PDBbind-trained models have different training provenance.
Sources
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table
Profile review details
Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.
Stable record: reported-task-77a32496ce8fe6Specifications
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 |
|---|---|
| Datasets | PDBbind v2020 refined/core collections and BindingDB-trained component models.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Splits | CASF2016 core complexes are excluded from refined-set training; a filtered general-set subset excludes refined/core entries.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Metrics | RMSE and Pearson correlation coefficient for binding-affinity evaluation; target-specific ranking collections are reported separately.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Baselines | Table 4 compares the docking and sequence-model components with HAC-Net, FAST’s pretrained SG-CNN and KDeep. Training sets and affinity units differ: HAC-Net is retrained, SG-CNN retains its original training, and KDeep uses limited retraining or its server default. PerSpect ML is discussed from published results rather than rerun.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods: Comparison to Other Tools; Results: Comparison with Structure-Based Tools; Table 4 |
| Leakage controls | PDBbind fine-tuning excludes the CoreSet, and the filtered GeneralSet excludes both RefinedSet and CoreSet. The separate three-target ranking test additionally enforces less than 30% protein similarity. These distinct controls do not establish a BindingDB-overlap audit for the CASF-2016 or GeneralSet rows.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods: Fine-Tuning of the BDB-Trained Models Using PDBbind; GeneralSet Benchmark; Ranking Benchmark |
| Uncertainty | One PDBbind model group uses repeated five-fold cross-validation.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Entity type | Paper-specific computational evaluation protocol.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Organisms | CASF-2016 and the filtered PDBbind2020 GeneralSet pool protein–ligand complexes. The dataset and benchmark methods do not provide a species census for the scored subsets; target accession identities, rather than an assumed single organism, define their biological context. · Not reported in inspected sourcesSourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Methods: GeneralSet Benchmark and data preparation; Table 4 |
| Assays | Protein–ligand affinity and structural labels.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Allowed inputs | Protein–ligand representations used by component scoring models.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
| Adaptation | Supervised meta-model/component fitting; CASF core examples are excluded from the refined training collection.SourcesImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Papers and result coverage
Last literature check: 2026-09-17. Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling | PMC archival version PMC11632770.1 | Read source |
Historical gaps recorded on 2026-09-17
The catalogue now holds 1 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.
- complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.
- exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.
Search and extraction details
primary comparison tables located
Searches
- Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling 10.1021/acs.jcim.4c01116
Evidence locations
- Table 2; XML table tbl2
- Table 3; XML table tbl3
- Table 4; XML table tbl4
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.
17 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets PDBbind v2020 refined/core collections and BindingDB-trained component models. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits CASF2016 core complexes are excluded from refined-set training; a filtered general-set subset excludes refined/core entries. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised meta-model/component fitting; CASF core examples are excluded from the refined training collection. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics RMSE and Pearson correlation coefficient for binding-affinity evaluation; target-specific ranking collections are reported separately. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines Table 4 compares the docking and sequence-model components with HAC-Net, FAST’s pretrained SG-CNN and KDeep. Training sets and affinity units differ: HAC-Net is retrained, SG-CNN retains its original training, and KDeep uses limited retraining or its server default. PerSpect ML is discussed from published results rather than rerun. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Methods: Comparison to Other Tools; Results: Comparison with Structure-Based Tools; Table 4 Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls PDBbind fine-tuning excludes the CoreSet, and the filtered GeneralSet excludes both RefinedSet and CoreSet. The separate three-target ranking test additionally enforces less than 30% protein similarity. These distinct controls do not establish a BindingDB-overlap audit for the CASF-2016 or GeneralSet rows. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Methods: Fine-Tuning of the BDB-Trained Models Using PDBbind; GeneralSet Benchmark; Ranking Benchmark Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty One PDBbind model group uses repeated five-fold cross-validation. Individual claims | Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table Version: PMC archival version PMC11632770.1 | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
2 source records and release history
- Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Original source · PMC archival version PMC11632770.1
- Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling · Original source · PMC archival version PMC11632770.1
Technical metadata and extraction receipts
Stable ID: reported-task-77a32496ce8fe6
- areas
- molecular-interactions
- tasks
- protein-ligand binding affinity prediction
- entity level
- task
- version
- Not reported
- task
- protein-ligand binding affinity prediction
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_tables_located; primary sources: evidence-expansion-ligand-affinity-meta-model-2024-0be25fe7; inspected locators: Table 2; XML table tbl2; Table 3; XML table tbl3; Table 4; XML table tbl4; searched queries: Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling 10.1021/acs.jcim.4c01116; gaps: complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.; exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.; claim scope: Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
- historical missing metadata
- protocol version: 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
- benchmark
- entity classification
- review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: ligand-affinity-meta-model-2024; source locator: Dataset Selection: PDBbind; GeneralSet Benchmark; model training; cached text lines 16, 38, 55–56; task metric definitions and corresponding results table; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.
Related records
- dataset: CASF-2016
- benchmark: binding-affinity meta-model: protein-ligand binding affinity prediction