Davis kinase binding affinities (pKd), cold-target split: test proteins unseen in training and validation.
3 evaluations · 18 results. Different protocols are not a single leaderboard.
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) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.756 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'CI' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.453 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'MSE' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.514 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'Pearson' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.24 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'rm2' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.673 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'RMSE' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.45 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 sourceLLF 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 'LLF', strategy 'Cold-target', dataset 'Davis', column 'Spearman' (text-layer line 443) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.841 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'CI' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.292 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'MSE' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.727 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'Pearson' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.485 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'rm2' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.541 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'RMSE' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-target split Dataset: Davis cold-target test set (PLABench) | 0.586 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 sourceMixingDTA 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 'MixingDTA', strategy 'Cold-target', dataset 'Davis', column 'Spearman' (text-layer line 444) |
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6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Population Standardised Davis: 25,772 pairs, 379 unique sequences, 68 ligands (Methods lines 363-369) Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Table 2; Methods 'Datasets' Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Split cold-target split: test proteins unseen in training and validation Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Version Davis as split by MixingDTA (PLABench) Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Davis kinase binding affinities (pKd), cold-target split: test proteins unseen in training and validation. Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name Davis cold-target test set (PLABench) Context-only references | Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction Table 2; Methods 'Datasets' Version: bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF | not individually reviewed No individual claim review recorded 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-data-davis-cold-target