Configuration

SimBoost with Similarity features protein and Similarity features drug representation

SimBoost with Similarity features protein and Similarity features drug representation: Protein representation: Similarity features; drug representation: Similarity features.

2 evaluations · 4 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

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)
SimBoost with Similarity features protein and Similarity features drug representationEvaluation	Davis drug-target affinity, DeepDTA holdout fold, Davis holdout test fold (DeepDTA split)	0.885	Not reported	0.246	Not reported
SimBoost with Similarity features protein and Similarity features drug representationEvaluation	KIBA drug-target affinity, DeepDTA holdout fold, KIBA holdout test fold (DeepDTA split)	Not reported	0.836	Not reported	0.222

All 4 result rows with coverage, uncertainty and sources

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: SimBoost with Similarity features protein and Similarity features drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.885 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 SimBoost (Similarity features; Similarity features) 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 'SimBoost' with protein 'Similarity features' and drug 'Similarity features', column 'CI' (text-layer line 524)
Configuration: SimBoost with Similarity features protein and Similarity features drug representation	Protocol: Davis drug-target affinity, DeepDTA holdout fold Dataset: Davis holdout test fold (DeepDTA split)	0.246 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 SimBoost (Similarity features; Similarity features) 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 'SimBoost' with protein 'Similarity features' and drug 'Similarity features', column 'MSE' (text-layer line 524)
Configuration: SimBoost with Similarity features protein and Similarity features drug representation	Protocol: KIBA drug-target affinity, DeepDTA holdout fold Dataset: KIBA holdout test fold (DeepDTA split)	0.836 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 SimBoost (Similarity features; Similarity features) 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 'SimBoost' with protein 'Similarity features' and drug 'Similarity features', column 'CI' (text-layer line 584)
Configuration: SimBoost with Similarity features protein and Similarity features drug representation	Protocol: KIBA drug-target affinity, DeepDTA holdout fold Dataset: KIBA holdout test fold (DeepDTA split)	0.222 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 SimBoost (Similarity features; Similarity features) 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 'SimBoost' with protein 'Similarity features' and drug 'Similarity features', column 'MSE' (text-layer line 584)

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

Use this configuration

How it works, versions and access

Strengths, limitations and unresolved questions

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.

0 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

No evidence rows match these filters. Choose another scope or clear the search.

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

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: lit-interactions-20261011-plmdta-config-simboost-similarity-features-similarity-features

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

Related records

configuration of: SimBoost
system: SimBoost (Similarity features; Similarity features) on Davis
system: SimBoost (Similarity features; Similarity features) on KIBA

Suggest a correction