Configuration

GraphDTA with 1D conv. protein and GAT drug representation

GraphDTA with 1D conv. protein and GAT drug representation: Protein representation: 1D conv.; drug representation: GAT.

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Evaluations and results

2 evaluations · 4 results. Different protocols are not a single leaderboard.

Sorted by Concordance index (Davis DeepDTA holdout fold) (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 (Davis DeepDTA holdout fold)	Concordance index (KIBA DeepDTA holdout fold)	MSE (Davis DeepDTA holdout fold)	MSE (KIBA DeepDTA holdout fold)
GraphDTA with 1D conv. protein and GAT drug representationEvaluation	Davis drug-target affinity, DeepDTA holdout fold, Davis holdout test fold (DeepDTA split)	0.893	Not reported	0.255	Not reported
GraphDTA with 1D conv. protein and GAT drug representationEvaluation	KIBA drug-target affinity, DeepDTA holdout fold, KIBA holdout test fold (DeepDTA split)	Not reported	0.866	Not reported	0.178

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GraphDTA with 1D conv. protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.893 Concordance index (Davis DeepDTA holdout fold) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 3 KIBA rows KronRLS, SimBoost and DeepDTA equal the values printed in Ozturk et al. 2018, while the proposed models are 5-seed means; the rows may not share a training run or protocol. Do not compare these three rows against the proposed models automatically.Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, rows KronRLS, SimBoost, DeepDTA Methods, coverage and source GraphDTA (1D conv.; GAT) on Davis lit-interactions-20261011-plmdta-protocol-davis-deepdta-holdout Aggregation: Not reported Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 2, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'CI' (text-layer line 526)
Configuration: GraphDTA with 1D conv. protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.255 MSE (Davis DeepDTA holdout fold) unit-unreported · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 3 KIBA rows KronRLS, SimBoost and DeepDTA equal the values printed in Ozturk et al. 2018, while the proposed models are 5-seed means; the rows may not share a training run or protocol. Do not compare these three rows against the proposed models automatically.Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, rows KronRLS, SimBoost, DeepDTA Methods, coverage and source GraphDTA (1D conv.; GAT) on Davis lit-interactions-20261011-plmdta-protocol-davis-deepdta-holdout Aggregation: Not reported Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 2, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'MSE' (text-layer line 526)
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)	0.866 Concordance index (KIBA DeepDTA holdout fold) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Evaluation origin not reported · Source checked Evidence concern: excluded from comparisons Table 3 KIBA rows KronRLS, SimBoost and DeepDTA equal the values printed in Ozturk et al. 2018, while the proposed models are 5-seed means; the rows may not share a training run or protocol. Do not compare these three rows against the proposed models automatically.Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, rows KronRLS, SimBoost, DeepDTA Methods, coverage and source GraphDTA (1D conv.; GAT) on KIBA lit-interactions-20261011-plmdta-protocol-kiba-deepdta-holdout Aggregation: Not reported Comprehensive 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)
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)	0.178 MSE (KIBA DeepDTA holdout fold) unit-unreported · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Evaluation origin not reported · Source checked Evidence concern: excluded from comparisons Table 3 KIBA rows KronRLS, SimBoost and DeepDTA equal the values printed in Ozturk et al. 2018, while the proposed models are 5-seed means; the rows may not share a training run or protocol. Do not compare these three rows against the proposed models automatically.Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, rows KronRLS, SimBoost, DeepDTA Methods, coverage and source GraphDTA (1D conv.; GAT) on KIBA lit-interactions-20261011-plmdta-protocol-kiba-deepdta-holdout Aggregation: Not reported Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction · Table 3, row 'GraphDTA' with protein '1D conv.' and drug 'GAT', column 'MSE' (text-layer line 586)

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

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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-config-graphdta-1d-conv-gat

areas: molecular-interactions
contexts: research
method types: supervised_machine_learning
reported name: GraphDTA (1D conv.; GAT)
foundation model eligible: false
parameters: Protein representation: 1D conv.; drug representation: GAT
source locator: Tables 2 and 3, row 'GraphDTA' with protein '1D conv.' and drug 'GAT'
access reuse: Not stated in the source
missing metadata: version: reason: unreported; note: No checkpoint version printed
provenance note: GraphDTA configuration as printed in Tables 2 and 3.

Related records

configuration of: GraphDTA
system: GraphDTA (1D conv.; GAT) on Davis
system: GraphDTA (1D conv.; GAT) on KIBA

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