Protocol

PLABench KIBA, cold-drug split

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

3 evaluations · 18 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

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.

View coverage and remaining gaps across all benchmarks

Results

Results are available, but no reviewed comparison panel is linked in this release.

All evaluations

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

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

Sorted by Concordance index (PLABench KIBA-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 KIBA-cold-drug)	Modified squared correlation (PLABench KIBA-cold-drug)	MSE (PLABench KIBA-cold-drug)	Pearson (PLABench KIBA-cold-drug)	RMSE (PLABench KIBA-cold-drug)	Spearman (PLABench KIBA-cold-drug)
MixingDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.756 (best in column)	0.432 (best in column)	0.34 (best in column)	0.68 (best in column)	0.583 (best in column)	0.65 (best in column)
LLF (retrained on Davis and KIBA, PLABench)Evaluation	0.715	0.247	0.457	0.548	0.676	0.566
DeepDTA (retrained on Davis and KIBA, PLABench)Evaluation	0.712	0.285	0.439	0.565	0.662	0.556

All 18 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

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 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: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.715 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'CI' (text-layer line 446)
Configuration: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.457 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'MSE' (text-layer line 446)
Configuration: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.548 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'Pearson' (text-layer line 446)
Configuration: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.247 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'rm2' (text-layer line 446)
Configuration: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.676 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'RMSE' (text-layer line 446)
Configuration: LLF (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.566 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 LLF 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 'LLF', strategy 'Cold-drug', dataset 'KIBA', column 'Spearman' (text-layer line 446)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.756 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'CI' (text-layer line 447)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.34 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'MSE' (text-layer line 447)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.68 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'Pearson' (text-layer line 447)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.432 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'rm2' (text-layer line 447)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.583 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'RMSE' (text-layer line 447)
Configuration: MixingDTA (retrained on Davis and KIBA, PLABench)	Protocol: PLABench KIBA, cold-drug split Dataset: KIBA cold-drug test set (PLABench)	0.65 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 MixingDTA 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 'MixingDTA', strategy 'Cold-drug', dataset 'KIBA', column 'Spearman' (text-layer line 447)

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

Methods and evaluation design

Procedure, tasks and evaluated configurations

Recorded evaluations

Each evaluation records what was tested and under which conditions.

Baseline coverage

Reference methods help show what a model adds beyond simple controls. We track a null control and a conventional method for each protocol.

0 of 2 active baseline roles have published Rewire measurements in this release. Measurements on a selected protocol do not establish coverage of an entire suite.

No execution recipe linked to this protocol. Recipe availability does not establish a completed evaluation.

External evaluations: 3

Literature evidence is not a Rewire measurement. Executed but unpublished runs and private review status are not included.

Null control

Proposed control: requires review

Training-set mean where supervised fitting is permitted

Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.

This is a suggested selection rule, not a validated method or a measured score.

Conventional reference

Proposed control: requires review

Simple features with train-only ridge regression

Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.

This is a suggested selection rule, not a validated method or a measured score.

Protocol coverage CSV (gzip) · Model evaluation matrix (gzip) · Source table (gzip) · Release and checksums (gzip)

Coverage is derived from release 2026-10-23-5347695500ed. Source citations describe the original records; they do not validate an unreviewed baseline proposal. No results have been generated by this audit.

Run instructions

No runnable recipe has been reviewed for this protocol. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

0 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance

No evidence rows match these filters. Choose another scope or clear the search.

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release history

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

Download this release (gzip)

Technical metadata and extraction receipts

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

areas: molecular-interactions
contexts: research
protocol: KIBA 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, 'KIBA' columns, strategy 'Cold-drug'

Related records

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

Suggest a correction