Protocol

PLABench Davis, cold-drug split

Affinity regression on Davis, cold-drug split: test ligands unseen in training and validation; models retrained by the benchmark authors.

3 evaluations · 18 results

Overview

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3 recorded evaluations, 18 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

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All evaluations

3 evaluations · 18 results. Different protocols are not a single leaderboard.

Protocol: PLABench Davis, cold-drug split · Dataset: Davis cold-drug test set (PLABench)

Sorted by Concordance index (PLABench Davis-cold-drug) (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-drug)	Modified squared correlation (PLABench Davis-cold-drug)	MSE (PLABench Davis-cold-drug)	Pearson (PLABench Davis-cold-drug)	RMSE (PLABench Davis-cold-drug)	Spearman (PLABench Davis-cold-drug)
MixingDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.738 (best in column)	0.211 (best in column)	0.54 (best in column)	0.515 (best in column)	0.735 (best in column)	0.447 (best in column)
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.68	0.086	0.659	0.33	0.811	0.344
LLF (retrained on Davis and KIBA, PLABench)Evaluation	0.663	0.053	0.711	0.278	0.843	0.313

All 18 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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)

Source checking is not independent reproduction. Release 2026-10-23-5347695500ed.

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Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Original source · bioRxiv 10.64898/2026.09.17.752433v1 (posted 2026-09-23); 30-page PDF

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Technical metadata and extraction receipts

Stable ID: lit-interactions-20261011-leakctl-protocol-davis-cold-drug

areas: molecular-interactions
contexts: research
protocol: Davis cold-drug test set; MSE, RMSE, Pearson, Spearman, rm2 and CI as printed in Table 2
version: PLABench bioRxiv v1
limitations: Metrics are computed over all test pairs of the set (Methods, 'Evaluation metrics'); per-target averaging is not stated for these tables.; Only one value per cell is printed; no uncertainty is reported in Tables 2-4.; Only the three sequence-based models (DeepDTA, LLF, MixingDTA) were retrained on Davis and KIBA.
source locator: Table 2, 'Davis' columns, strategy 'Cold-drug'

Related records

part of: PLABench
uses data: Davis cold-drug test set (PLABench)
assessment: DeepDTA on PLABench Davis-cold-drug
assessment: LLF on PLABench Davis-cold-drug
assessment: MixingDTA on PLABench Davis-cold-drug

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