KIBA kinase inhibitor bioactivities (unified KIBA score), 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 KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.698 Concordance index (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'CI' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.511 MSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'MSE' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.558 Pearson (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Pearson' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.297 Modified squared correlation (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'rm2' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.714 RMSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'RMSE' (text-layer line 442) |
| Configuration: DeepDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.522 Spearman (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Spearman' (text-layer line 442) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.795 Concordance index (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'CI' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.343 MSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'MSE' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.726 Pearson (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Pearson' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.523 Modified squared correlation (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'rm2' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.585 RMSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'RMSE' (text-layer line 443) |
| Configuration: LLF (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.735 Spearman (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Spearman' (text-layer line 443) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.761 Concordance index (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'CI' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.358 MSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'MSE' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.712 Pearson (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Pearson' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.502 Modified squared correlation (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'rm2' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.598 RMSE (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'RMSE' (text-layer line 444) |
| Configuration: MixingDTA (retrained on Davis and KIBA, PLABench) | Protocol: PLABench KIBA, cold-target split Dataset: KIBA cold-target test set (PLABench) | 0.656 Spearman (PLABench KIBA-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 KIBA-cold-target lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', 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 TDC KIBA: 117,657 pairs, 229 proteins, 2,068 ligands (Methods lines 382-384) 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 KIBA 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 KIBA kinase inhibitor bioactivities (unified KIBA score), 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 KIBA 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-kiba-cold-target