DeepDTA: description
Description of DeepDTA
Deep learning model that predicts drug-target binding affinity from a ligand SMILES string and a protein sequence, using a separate one-dimensional convolutional encoder for each.
Replace a placeholder description with a sourced functional description (issue #88). Historical releases retain the original value.
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.
14 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field description Context-only references | DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Claimed field description Context-only references | DeepDTA repository README (hkmztrk/DeepDTA) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: a546a8433a6822e958f36171c4356ad6f414d623 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Previous value Base method introduced by Öztürk et al. (2018). Evaluated configurations, retrained weights and their scores are separate records. Context-only references | DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Previous value Base method introduced by Öztürk et al. (2018). Evaluated configurations, retrained weights and their scores are separate records. Context-only references | DeepDTA repository README (hkmztrk/DeepDTA) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: a546a8433a6822e958f36171c4356ad6f414d623 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Source location source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Context-only references | DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Source location source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Context-only references | DeepDTA repository README (hkmztrk/DeepDTA) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: a546a8433a6822e958f36171c4356ad6f414d623 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Claimed value Deep learning model that predicts drug-target binding affinity from a ligand SMILES string and a protein sequence, using a separate one-dimensional convolutional encoder for each. Context-only references | DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Claimed value Deep learning model that predicts drug-target binding affinity from a ligand SMILES string and a protein sequence, using a separate one-dimensional convolutional encoder for each. Context-only references | DeepDTA repository README (hkmztrk/DeepDTA) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: a546a8433a6822e958f36171c4356ad6f414d623 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Description Replace a placeholder description with a sourced functional description (issue #88). Historical releases retain the original value. Context-only references | DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
| Description Replace a placeholder description with a sourced functional description (issue #88). Historical releases retain the original value. Context-only references | DeepDTA repository README (hkmztrk/DeepDTA) source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: a546a8433a6822e958f36171c4356ad6f414d623 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the retrieved original artifact bytes |
Sources and history
Release 2026-11-02-a7f03c9e55a5 · Record review: source checked
2 source records and release history
- DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · Original source · 1801.10193v2
- DeepDTA repository README (hkmztrk/DeepDTA) · Original source · a546a8433a6822e958f36171c4356ad6f414d623
Technical metadata and extraction receipts
Stable ID: metadata-correction-1c05d427cdd351dd8e0b
- areas
- molecular-interactions
- field
- description
- previous value
- Base method introduced by Öztürk et al. (2018). Evaluated configurations, retrained weights and their scores are separate records.
- value
- Deep learning model that predicts drug-target binding affinity from a ligand SMILES string and a protein sequence, using a separate one-dimensional convolutional encoder for each.
- source locator
- source-label-deepdta-arxiv-3c368fed: abstract, p.1 ('a deep-learning based model that uses only sequence information of both targets and drugs ... modeling of protein sequences and compound 1D representations with convolutional neural networks'); the record's reviewed profile summary
- review
- method: automated-source-review; ai-assisted-source-review; reviewer: claude; reviewer note: Extractor: Claude integrator agent for issue #88. Reviewer: a separate Claude review agent, independent of the extractor; no human review claimed.; date: 2026-10-11; method note: Compared the store diff with origin/main, read each new description against its cited source passage and checked the licence of each new source record.; note: Approved. Checked the 10 method and model descriptions against their sources (EVmutation, GEMME and RSALOR abstracts, the FramePool k-mer random forests, the RhoMax BLASSO text) and spot-checked the 49 mRNABench rows against Appendix A; all accurate. Licences on the 3 new sources match their Europe PMC records. At the reviewer's suggestion, the en dash printed in some checkpoint names is written as a hyphen in the descriptions.; receipt: data/omics/qa-descriptions-20261011-a/review.json
Related records
- subject: DeepDTA