Davis kinase binding affinities (pKd), cold-drug split: test ligands 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-drug split Dataset: Davis cold-drug test set (PLABench) | 0.68 Concordance index (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'CI' (text-layer line 445) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.659 MSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'MSE' (text-layer line 445) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.33 Pearson (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'Pearson' (text-layer line 445) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.086 Modified squared correlation (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'rm2' (text-layer line 445) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.811 RMSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'RMSE' (text-layer line 445) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.344 Spearman (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Cold-drug', dataset 'Davis', column 'Spearman' (text-layer line 445) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.663 Concordance index (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'CI' (text-layer line 446) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.711 MSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'MSE' (text-layer line 446) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.278 Pearson (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'Pearson' (text-layer line 446) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.053 Modified squared correlation (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'rm2' (text-layer line 446) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.843 RMSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'RMSE' (text-layer line 446) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.313 Spearman (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'LLF', strategy 'Cold-drug', dataset 'Davis', column 'Spearman' (text-layer line 446) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.738 Concordance index (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'CI' (text-layer line 447) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.54 MSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'MSE' (text-layer line 447) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.515 Pearson (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'Pearson' (text-layer line 447) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.211 Modified squared correlation (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'rm2' (text-layer line 447) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.735 RMSE (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'RMSE' (text-layer line 447) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench Davis, cold-drug split Dataset: Davis cold-drug test set (PLABench) | 0.447 Spearman (PLABench Davis-cold-drug) 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-drug lit-interactions-20261011-leakctl-protocol-davis-cold-drug Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'MixingDTA', strategy 'Cold-drug', dataset 'Davis', column 'Spearman' (text-layer line 447) |
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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-drug split: test ligands 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-drug split: test ligands 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-drug 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-drug