Configuration

DeepDTA (retrained on Davis and KIBA, PLABench)

Configuration of DeepDTA: Retrained by the benchmark authors on Davis or KIBA under each split (warm: 5-fold CV on an 80/20 split; cold splits: two-stage protocol from MixingDTA, five fold models evaluated on a fixed test set and aggregated).

6 evaluations · 36 results

Overview

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Evaluations and results

6 evaluations · 36 results. Different protocols are not a single leaderboard.

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	Protocol and dataset	Concordance index (PLABench Davis-cold-drug)	Concordance index (PLABench Davis-cold-target)	Concordance index (PLABench Davis-warm)	Concordance index (PLABench KIBA-cold-drug)	Concordance index (PLABench KIBA-cold-target)	Concordance index (PLABench KIBA-warm)	Modified squared correlation (PLABench Davis-cold-drug)	Modified squared correlation (PLABench Davis-cold-target)	Modified squared correlation (PLABench Davis-warm)	Modified squared correlation (PLABench KIBA-cold-drug)	Modified squared correlation (PLABench KIBA-cold-target)	Modified squared correlation (PLABench KIBA-warm)	MSE (PLABench Davis-cold-drug)	MSE (PLABench Davis-cold-target)	MSE (PLABench Davis-warm)	MSE (PLABench KIBA-cold-drug)	MSE (PLABench KIBA-cold-target)	MSE (PLABench KIBA-warm)	Pearson (PLABench Davis-cold-drug)	Pearson (PLABench Davis-cold-target)	Pearson (PLABench Davis-warm)	Pearson (PLABench KIBA-cold-drug)	Pearson (PLABench KIBA-cold-target)	Pearson (PLABench KIBA-warm)	RMSE (PLABench Davis-cold-drug)	RMSE (PLABench Davis-cold-target)	RMSE (PLABench Davis-warm)	RMSE (PLABench KIBA-cold-drug)	RMSE (PLABench KIBA-cold-target)	RMSE (PLABench KIBA-warm)	Spearman (PLABench Davis-cold-drug)	Spearman (PLABench Davis-cold-target)	Spearman (PLABench Davis-warm)	Spearman (PLABench KIBA-cold-drug)	Spearman (PLABench KIBA-cold-target)	Spearman (PLABench KIBA-warm)
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench Davis, cold-drug split, Davis cold-drug test set (PLABench)	0.68	Not reported	Not reported	Not reported	Not reported	Not reported	0.086	Not reported	Not reported	Not reported	Not reported	Not reported	0.659	Not reported	Not reported	Not reported	Not reported	Not reported	0.33	Not reported	Not reported	Not reported	Not reported	Not reported	0.811	Not reported	Not reported	Not reported	Not reported	Not reported	0.344	Not reported	Not reported	Not reported	Not reported	Not reported
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench Davis, cold-target split, Davis cold-target test set (PLABench)	Not reported	0.8	Not reported	Not reported	Not reported	Not reported	Not reported	0.359	Not reported	Not reported	Not reported	Not reported	Not reported	0.381	Not reported	Not reported	Not reported	Not reported	Not reported	0.625	Not reported	Not reported	Not reported	Not reported	Not reported	0.617	Not reported	Not reported	Not reported	Not reported	Not reported	0.521	Not reported	Not reported	Not reported	Not reported
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench Davis, warm split, Davis warm test set (PLABench)	Not reported	Not reported	0.875	Not reported	Not reported	Not reported	Not reported	Not reported	0.625	Not reported	Not reported	Not reported	Not reported	Not reported	0.232	Not reported	Not reported	Not reported	Not reported	Not reported	0.816	Not reported	Not reported	Not reported	Not reported	Not reported	0.482	Not reported	Not reported	Not reported	Not reported	Not reported	0.658	Not reported	Not reported	Not reported
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench KIBA, cold-drug split, KIBA cold-drug test set (PLABench)	Not reported	Not reported	Not reported	0.712	Not reported	Not reported	Not reported	Not reported	Not reported	0.285	Not reported	Not reported	Not reported	Not reported	Not reported	0.439	Not reported	Not reported	Not reported	Not reported	Not reported	0.565	Not reported	Not reported	Not reported	Not reported	Not reported	0.662	Not reported	Not reported	Not reported	Not reported	Not reported	0.556	Not reported	Not reported
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench KIBA, cold-target split, KIBA cold-target test set (PLABench)	Not reported	Not reported	Not reported	Not reported	0.698	Not reported	Not reported	Not reported	Not reported	Not reported	0.297	Not reported	Not reported	Not reported	Not reported	Not reported	0.511	Not reported	Not reported	Not reported	Not reported	Not reported	0.558	Not reported	Not reported	Not reported	Not reported	Not reported	0.714	Not reported	Not reported	Not reported	Not reported	Not reported	0.522	Not reported
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	PLABench KIBA, warm split, KIBA warm test set (PLABench)	Not reported	Not reported	Not reported	Not reported	Not reported	0.831	Not reported	Not reported	Not reported	Not reported	Not reported	0.657	Not reported	Not reported	Not reported	Not reported	Not reported	0.23	Not reported	Not reported	Not reported	Not reported	Not reported	0.818	Not reported	Not reported	Not reported	Not reported	Not reported	0.479	Not reported	Not reported	Not reported	Not reported	Not reported	0.798

All 36 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: 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: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.875 Concordance index (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'CI' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.232 MSE (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'MSE' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.816 Pearson (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'Pearson' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.625 Modified squared correlation (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'rm2' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.482 RMSE (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'RMSE' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench Davis, warm split Dataset: Davis warm test set (PLABench)	0.658 Spearman (PLABench Davis-warm) 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-warm lit-interactions-20261011-leakctl-protocol-davis-warm Aggregation: Not reported Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction · Table 2, row 'DeepDTA', strategy 'Warm split', dataset 'Davis', column 'Spearman' (text-layer line 439)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.712 Concordance index (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'CI' (text-layer line 445)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.439 MSE (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'MSE' (text-layer line 445)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.565 Pearson (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Pearson' (text-layer line 445)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.285 Modified squared correlation (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'rm2' (text-layer line 445)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.662 RMSE (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'RMSE' (text-layer line 445)
Configuration: DeepDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.556 Spearman (PLABench KIBA-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 KIBA-cold-drug lit-interactions-20261011-leakctl-protocol-kiba-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 'KIBA', column 'Spearman' (text-layer line 445)
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 checked Methods, coverage and source DeepDTA 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)

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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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Stable ID: lit-interactions-20261011-leakctl-config-deepdta-davis-kiba

areas: molecular-interactions
contexts: research
method types: supervised_machine_learning
reported name: DeepDTA
foundation model eligible: false
parameters: Retrained by the benchmark authors on Davis or KIBA under each split (warm: 5-fold CV on an 80/20 split; cold splits: two-stage protocol from MixingDTA, five fold models evaluated on a fixed test set and aggregated)
source locator: Table 1; Methods; Table 2
access reuse: Not stated per model in the source; see each model's release
missing metadata: version: reason: unreported; note: No checkpoint version printed
provenance note: DeepDTA as evaluated in PLABench (sequence-based).

Related records

configuration of: DeepDTA
system: DeepDTA on PLABench Davis-cold-drug
system: DeepDTA on PLABench Davis-cold-target
system: DeepDTA on PLABench Davis-warm
system: DeepDTA on PLABench KIBA-cold-drug
system: DeepDTA on PLABench KIBA-cold-target
system: DeepDTA on PLABench KIBA-warm

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