Protocol

PLABench Davis, cold-target split

Affinity regression on Davis, cold-target split: test proteins 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-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)
MixingDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.841 (best in column)	0.485 (best in column)	0.292 (best in column)	0.727 (best in column)	0.541 (best in column)	0.586 (best in column)
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.8	0.359	0.381	0.625	0.617	0.521
LLF (retrained on Davis and KIBA, PLABench)Evaluation	0.756	0.24	0.453	0.514	0.673	0.45

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-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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source DeepDTA 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source LLF 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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 checked Methods, coverage and source MixingDTA 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)

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-target

areas: molecular-interactions
contexts: research
protocol: Davis cold-target 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-target'

Related records

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

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