Dataset

Davis holdout test fold (DeepDTA split)

Davis kinase inhibitor selectivity dataset: 442 kinases, 68 inhibitors, 30,056 pairs; Kd log-transformed to pKd following He et al. 2017; predefined DeepDTA holdout test fold. Evaluated on the corrected version with mutant sequences resolved (Appendix A); not the commonly used DeepDTA repository sequences.

Evaluation results

16 evaluations · 32 results. Different protocols are not a single leaderboard.

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 with 1D conv. protein and 1D conv. drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.902 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 DeepDTA (1D conv.; 1D conv.) 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 'DeepDTA' with protein '1D conv.' and drug '1D conv.', column 'CI' (text-layer line 529)
Configuration: DeepDTA with 1D conv. protein and 1D conv. drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.224 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 DeepDTA (1D conv.; 1D conv.) 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 'DeepDTA' with protein '1D conv.' and drug '1D conv.', column 'MSE' (text-layer line 529)
Configuration: DeepGraphDTA with 1D conv. (16) protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.907 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. (16); 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 'DeepGraphDTA' with protein '1D conv. (16)' and drug 'GAT', column 'CI' (text-layer line 534)
Configuration: DeepGraphDTA with 1D conv. (16) protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.176 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. (16); 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 'DeepGraphDTA' with protein '1D conv. (16)' and drug 'GAT', column 'MSE' (text-layer line 534)
Configuration: DeepGraphDTA with 1D conv. (16) protein and 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. (16); 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. (16)' and drug 'GCN', column 'CI' (text-layer line 533)
Configuration: DeepGraphDTA with 1D conv. (16) protein and GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.174 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. (16); 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. (16)' and drug 'GCN', column 'MSE' (text-layer line 533)
Configuration: DeepGraphDTA with 1D conv. (32) protein and GIN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.909 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. (32); GIN) 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. (32)' and drug 'GIN', column 'CI' (text-layer line 536)
Configuration: DeepGraphDTA with 1D conv. (32) protein and GIN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.172 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. (32); GIN) 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. (32)' and drug 'GIN', column 'MSE' (text-layer line 536)
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)
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-GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.889 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-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 'GraphDTA' with protein '1D conv.' and drug 'GAT-GCN', column 'CI' (text-layer line 525)
Configuration: GraphDTA with 1D conv. protein and GAT-GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.227 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-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 'GraphDTA' with protein '1D conv.' and drug 'GAT-GCN', column 'MSE' (text-layer line 525)
Configuration: GraphDTA with 1D conv. protein and GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.88 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.; 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 'GraphDTA' with protein '1D conv.' and drug 'GCN', column 'CI' (text-layer line 523)
Configuration: GraphDTA with 1D conv. protein and GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.237 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.; 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 'GraphDTA' with protein '1D conv.' and drug 'GCN', column 'MSE' (text-layer line 523)
Configuration: GraphDTA with 1D conv. protein and GIN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.897 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.; GIN) 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 'GIN', column 'CI' (text-layer line 527)
Configuration: GraphDTA with 1D conv. protein and GIN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.199 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.; GIN) 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 'GIN', column 'MSE' (text-layer line 527)
Configuration: KronRLS with Similarity vector protein and Similarity vector drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.87 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 KronRLS (Similarity vector; Similarity vector) 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 'KronRLS' with protein 'Similarity vector' and drug 'Similarity vector', column 'CI' (text-layer line 522)
Configuration: KronRLS with Similarity vector protein and Similarity vector drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.359 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 KronRLS (Similarity vector; Similarity vector) 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 'KronRLS' with protein 'Similarity vector' and drug 'Similarity vector', column 'MSE' (text-layer line 522)
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: PLM-GraphDTA with ESMC 600M protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.908 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 PLM-GraphDTA (ESMC 600M; 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 'PLM-GraphDTA' with protein 'ESMC 600M' and drug 'GAT', column 'CI' (text-layer line 535)
Configuration: PLM-GraphDTA with ESMC 600M protein and GAT drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.174 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 PLM-GraphDTA (ESMC 600M; 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 'PLM-GraphDTA' with protein 'ESMC 600M' and drug 'GAT', column 'MSE' (text-layer line 535)
Configuration: PLM-GraphDTA with ESMC 600M protein and GAT-GCN drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.91 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 PLM-GraphDTA (ESMC 600M; 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 'PLM-GraphDTA' with protein 'ESMC 600M' and drug 'GAT-GCN', column 'CI' (text-layer line 538)

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Evidence table

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5 evidence rows 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
Source location Section 3.1.1; Section 3.4 Context-only references	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Section 3.1.1; Section 3.4 Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_locator` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split Predefined DeepDTA holdout test fold (train:test 5:1) Context-only references	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Section 3.1.1; Section 3.4 Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.split` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version Davis as corrected by Monteiro et al. 2022 (mutant sequences resolved; Appendix A), DeepDTA folds Context-only references	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Section 3.1.1; Section 3.4 Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.version` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description Davis kinase inhibitor selectivity dataset: 442 kinases, 68 inhibitors, 30,056 pairs; Kd log-transformed to pKd following He et al. 2017; predefined DeepDTA holdout test fold. Evaluated on the corrected version with mutant sequences resolved (Appendix A); not the commonly used DeepDTA repository sequences. Context-only references	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Section 3.1.1; Section 3.4 Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `description` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name Davis holdout test fold (DeepDTA split) Context-only references	Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction Original source ↗ Section 3.1.1; Section 3.4 Version: bioRxiv 10.64898/2026.08.31.748056v1 (posted 2026-09-03); 24-page PDF Retrieved: 2026-10-11T21:27:40Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `name` Source artifact SHA-256: `11b88d5ab2964b581430454d0d9e7848739819d52ec2e338e2a6412cbd259163` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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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-data-davis-deepdta-holdout

areas: molecular-interactions
contexts: research
version: Davis as corrected by Monteiro et al. 2022 (mutant sequences resolved; Appendix A), DeepDTA folds
split: Predefined DeepDTA holdout test fold (train:test 5:1)
source locator: Section 3.1.1; Section 3.4

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