Configuration

DeepGraphDTA with 1D conv. (8) protein and GAT-GCN drug representation

DeepGraphDTA with 1D conv. (8) protein and GAT-GCN drug representation: Protein representation: 1D conv. (8); drug representation: GAT-GCN (kernel size in brackets selected by 5-fold CV).

1 evaluation · 2 results

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1 evaluation · 2 results. Different protocols are not a single leaderboard.

Protocol: Davis drug-target affinity, DeepDTA holdout fold · Dataset: Davis holdout test fold (DeepDTA split)

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	Concordance index (Davis DeepDTA holdout fold)	MSE (Davis DeepDTA holdout fold)
DeepGraphDTA with 1D conv. (8) protein and GAT-GCN drug representationEvaluation	0.906	0.175

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: DeepGraphDTA with 1D conv. (8) protein and GAT-GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.906 Concordance index (Davis DeepDTA holdout fold) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 DeepGraphDTA (1D conv. (8); GAT-GCN) 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 'DeepGraphDTA' with protein '1D conv. (8)' and drug 'GAT-GCN', column 'CI' (text-layer line 532)
Configuration: DeepGraphDTA with 1D conv. (8) protein and GAT-GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.175 MSE (Davis DeepDTA holdout fold) unit-unreported · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 DeepGraphDTA (1D conv. (8); GAT-GCN) 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 'DeepGraphDTA' with protein '1D conv. (8)' and drug 'GAT-GCN', column 'MSE' (text-layer line 532)

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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-deepgraphdta-1d-conv-8-gat-gcn

areas: molecular-interactions
contexts: research
method types: supervised_machine_learning
reported name: DeepGraphDTA (1D conv. (8); GAT-GCN)
foundation model eligible: false
parameters: Protein representation: 1D conv. (8); drug representation: GAT-GCN (kernel size in brackets selected by 5-fold CV)
source locator: Tables 2 and 3, row 'DeepGraphDTA' with protein '1D conv. (8)' and drug 'GAT-GCN'
access reuse: Code, trained models and data at https://github.com/matija-marijan/PLM-GraphDTA (source abstract)
missing metadata: version: reason: unreported; note: No checkpoint version printed
provenance note: DeepGraphDTA configuration as printed in Tables 2 and 3.

Related records

configuration of: DeepGraphDTA
system: DeepGraphDTA (1D conv. (8); GAT-GCN) on Davis

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