Configuration

PGraphDTA with ESM2 650M protein and GAT drug representation

PGraphDTA with ESM2 650M protein and GAT drug representation: Protein representation: ESM2 650M; drug representation: GAT.

2 evaluations · 4 results

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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)
PGraphDTA with ESM2 650M protein and GAT drug representationEvaluation	Davis drug-target affinity, DeepDTA holdout fold, Davis holdout test fold (DeepDTA split)	0.898	Not reported	0.206	Not reported
PGraphDTA with ESM2 650M protein and GAT drug representationEvaluation	KIBA drug-target affinity, DeepDTA holdout fold, KIBA holdout test fold (DeepDTA split)	Not reported	0.872	Not reported	0.16

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: PGraphDTA with ESM2 650M protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.898 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 PGraphDTA (ESM2 650M; 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 'PGraphDTA' with protein 'ESM2 650M' and drug 'GAT', column 'CI' (text-layer line 528)
Configuration: PGraphDTA with ESM2 650M protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.206 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 PGraphDTA (ESM2 650M; 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 'PGraphDTA' with protein 'ESM2 650M' and drug 'GAT', column 'MSE' (text-layer line 528)
Configuration: PGraphDTA with ESM2 650M protein and GAT drug representation	Protocol: KIBA drug-target affinity, DeepDTA holdout fold Dataset: KIBA holdout test fold (DeepDTA split)	0.872 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 PGraphDTA (ESM2 650M; 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 'PGraphDTA' with protein 'ESM2 650M' and drug 'GAT', column 'CI' (text-layer line 587)
Configuration: PGraphDTA with ESM2 650M protein and GAT drug representation	Protocol: KIBA drug-target affinity, DeepDTA holdout fold Dataset: KIBA holdout test fold (DeepDTA split)	0.16 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 PGraphDTA (ESM2 650M; 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 'PGraphDTA' with protein 'ESM2 650M' and drug 'GAT', column 'MSE' (text-layer line 587)

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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-pgraphdta-esm2-650m-gat

areas: molecular-interactions
contexts: research
method types: foundation_model
reported name: PGraphDTA (ESM2 650M; GAT)
foundation model eligible: true
parameters: Protein representation: ESM2 650M; drug representation: GAT
source locator: Tables 2 and 3, row 'PGraphDTA' with protein 'ESM2 650M' and drug 'GAT'
access reuse: Not stated in the source
version: ESM-2 650M, no contact maps (Section 3.3)
provenance note: PGraphDTA configuration as printed in Tables 2 and 3.

Related records

configuration of: PGraphDTA
uses model: ESM-2
system: PGraphDTA (ESM2 650M; GAT) on Davis
system: PGraphDTA (ESM2 650M; GAT) on KIBA

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