Published comparison transcribed from the pinned source; not reproduced.
1 evaluation · 6 results. Different protocols are not a single leaderboard.
Protocol: PLABench Davis, cold-target split · Dataset: Davis cold-target test set (PLABench)
Sorted by Concordance index (PLABench Davis-cold-target) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.
| Tested configuration | Concordance index (PLABench Davis-cold-target) | Modified squared correlation (PLABench Davis-cold-target) | MSE (PLABench Davis-cold-target) | Pearson (PLABench Davis-cold-target) | RMSE (PLABench Davis-cold-target) | Spearman (PLABench Davis-cold-target) |
|---|---|---|---|---|---|---|
| DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation | 0.8 | 0.359 | 0.381 | 0.625 | 0.617 | 0.521 |
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.8 Concordance index (PLABench Davis-cold-target) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'CI' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.381 MSE (PLABench Davis-cold-target) unit-unreported · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'MSE' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.625 Pearson (PLABench Davis-cold-target) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'Pearson' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.359 Modified squared correlation (PLABench Davis-cold-target) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'rm2' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.617 RMSE (PLABench Davis-cold-target) unit-unreported · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'RMSE' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.521 Spearman (PLABench Davis-cold-target) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepDTA on PLABench Davis-cold-target lit-interactions-20261011-leakctl-protocol-davis-cold-target Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-target', dataset 'Davis', column 'Spearman' (text-layer line 442) |
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PLABench Davis, cold-target split
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18 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Comparison: adaptation Retrained by the benchmark authors Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: aggregation Predictions of the five fold models on one fixed test set aggregated (Methods lines 389-394); the aggregation function is not stated Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: budget Not reported Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: dataset version PLABench bioRxiv v1 Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: inputs Protein sequence and ligand SMILES Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: metric implementation As defined in Methods, 'Evaluation metrics' Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: population Not reported Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: protocol id lit-interactions-20261011-leakctl-protocol-davis-cold-target Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: split Davis-cold-target Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Origin independent_paper Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2 row 'DeepDTA', column group 'strategy 'Cold-target', dataset 'Davis'' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-23-5347695500ed · Record review: source checked
Stable ID: lit-interactions-20261011-leakctl-eval-deepdta-davis-cold-target