Result

Concordance index (KIBA DeepDTA holdout fold) of GraphDTA with 1D conv. protein and GAT drug representation on KIBA holdout test fold (DeepDTA split)

0.866 Concordance index (KIBA DeepDTA holdout fold)

Methods

Tested configuration: GraphDTA with 1D conv. protein and GAT drug representation
Protocol: KIBA drug-target affinity, DeepDTA holdout fold
Dataset: KIBA holdout test fold (DeepDTA split)
Procedure: KIBA drug-target affinity, DeepDTA holdout fold
Evaluation: GraphDTA (1D conv.; GAT) on KIBA
Coverage: Not reported scored / Not reported eligible
Uncertainty: Not reported by the source
Evidence: Evaluation origin not reported · source checkedComprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'CI' (text-layer line 586)

A source-checked result verifies the numerical transcription, not every model or protocol detail. Evaluation metadata: source checked. Source checked does not mean independently reproduced.

Reproduction

Split: Holdout test fold
Adaptation: Not reported
Scoring implementation: CI and MSE as printed

No execution recipe has been verified for this exact configuration and evaluation. A benchmark's general instructions may use different inputs, splits or model settings.

Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.

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.

1 evidence row matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Reported result 0.866 Individual claims	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Table 3, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'CI' (text-layer line 586) Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	source checked ["source-hash-verification","pdf-text-parse","independent-cell-check","rendered-source-visual-check"] · 2026-10-11 unreported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Extraction: Parsed the pdftotext -layout text layer of the pinned bioRxiv PDF with extract/extract_plmdta.py; each row's method, protein and drug representation and two values asserted. Table 3, text-layer line 586. Independent review 2026-10-11: Re-downloaded the bioRxiv PDF: its hash differs because of the per-request banner, but the pdftotext -layout text layer hashes exactly to the recorded value, so the content checked is the content extracted. Parsed all 32 rows and 64 cells with an independent parser, mapping every result through its evaluation, configuration and protocol: all match and no cell was missed. Read 44 values covering both tables and every model family from rendered pages 10 and 11: 44 of 44 match, and the prose percentages (16.08, 0.89, 1.35 and 13.89 percent) follow from the printed cells. The three KIBA baseline rows were checked against the DeepDTA paper's own KIBA table, which is what the evidence concern records. Field: `attributes.printed_value` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact Extraction artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Extraction artifact

How to read the evidence

Sources and history

Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

1 source record and release history

Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Original source · bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF

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Technical metadata and extraction receipts

Stable ID: lit-interactions-20261011-plmdta-result-graphdta-1d-conv-gat-kiba-ci

metric: concordance-index
metric direction: higher
unit: unitless
printed value: 0.866
source locator: Table 3, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'CI' (text-layer line 586)
review: method: source-hash-verification; pdf-text-parse; independent-cell-check; rendered-source-visual-check; method note: Re-downloaded the bioRxiv PDF: its hash differs because of the per-request banner, but the pdftotext -layout text layer hashes exactly to the recorded value, so the content checked is the content extracted. Parsed all 32 rows and 64 cells with an independent parser, mapping every result through its evaluation, configuration and protocol: all match and no cell was missed. Read 44 values covering both tables and every model family from rendered pages 10 and 11: 44 of 44 match, and the prose percentages (16.08, 0.89, 1.35 and 13.89 percent) follow from the printed cells. The three KIBA baseline rows were checked against the DeepDTA paper's own KIBA table, which is what the evidence concern records.; reviewer: claude; reviewer note: Claude review agent reviewer-mi-2, independent of the extractor lit-microbial-interactions; no human review claimed; date: 2026-10-11; artifact sha256: 11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163; retrieval url: https://www.biorxiv.org/content/10.64898/2026.08.31.748056v1.full.pdf; note: Extraction: Parsed the pdftotext -layout text layer of the pinned bioRxiv PDF with extract/extract_plmdta.py; each row's method, protein and drug representation and two values asserted. Table 3, text-layer line 586. Independent review 2026-10-11: Re-downloaded the bioRxiv PDF: its hash differs because of the per-request banner, but the pdftotext -layout text layer hashes exactly to the recorded value, so the content checked is the content extracted. Parsed all 32 rows and 64 cells with an independent parser, mapping every result through its evaluation, configuration and protocol: all match and no cell was missed. Read 44 values covering both tables and every model family from rendered pages 10 and 11: 44 of 44 match, and the prose percentages (16.08, 0.89, 1.35 and 13.89 percent) follow from the printed cells. The three KIBA baseline rows were checked against the DeepDTA paper's own KIBA table, which is what the evidence concern records.
numeric value: 0.866
metric qualifier: KIBA DeepDTA holdout fold
missing metadata: uncertainty: reason: unreported

Related records

evaluation: GraphDTA (1D conv.; GAT) on KIBA

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