DeepAffinity: description
Description of DeepAffinity
Semi-supervised deep learning model that predicts compound-protein affinity from a compound SMILES string and a structure-annotated protein sequence, using recurrent encoders followed by convolutional layers.
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 | DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1806.07537v2 | 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 | DeepAffinity repository README (Shen-Lab/DeepAffinity) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: debca4c9f01991a37594e1b67f3dbb46a29e82d1 | 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 Karimi et al. (2019). Evaluated configurations, retrained weights and their scores are separate records. Context-only references | DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1806.07537v2 | 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 Karimi et al. (2019). Evaluated configurations, retrained weights and their scores are separate records. Context-only references | DeepAffinity repository README (Shen-Lab/DeepAffinity) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: debca4c9f01991a37594e1b67f3dbb46a29e82d1 | 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-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Context-only references | DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1806.07537v2 | 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-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Context-only references | DeepAffinity repository README (Shen-Lab/DeepAffinity) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: debca4c9f01991a37594e1b67f3dbb46a29e82d1 | 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 Semi-supervised deep learning model that predicts compound-protein affinity from a compound SMILES string and a structure-annotated protein sequence, using recurrent encoders followed by convolutional layers. Context-only references | DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1806.07537v2 | 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 Semi-supervised deep learning model that predicts compound-protein affinity from a compound SMILES string and a structure-annotated protein sequence, using recurrent encoders followed by convolutional layers. Context-only references | DeepAffinity repository README (Shen-Lab/DeepAffinity) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: debca4c9f01991a37594e1b67f3dbb46a29e82d1 | 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 | DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1806.07537v2 | 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 | DeepAffinity repository README (Shen-Lab/DeepAffinity) source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; the record's reviewed profile summary Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: debca4c9f01991a37594e1b67f3dbb46a29e82d1 | 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
- DeepAffinity: interpretable deep learning of compound–protein affinity (arXiv:1806.07537v2) · Original source · 1806.07537v2
- DeepAffinity repository README (Shen-Lab/DeepAffinity) · Original source · debca4c9f01991a37594e1b67f3dbb46a29e82d1
Technical metadata and extraction receipts
Stable ID: metadata-correction-a334f528684f553912a9
- areas
- molecular-interactions
- field
- description
- previous value
- Base method introduced by Karimi et al. (2019). Evaluated configurations, retrained weights and their scores are separate records.
- value
- Semi-supervised deep learning model that predicts compound-protein affinity from a compound SMILES string and a structure-annotated protein sequence, using recurrent encoders followed by convolutional layers.
- source locator
- source-label-deepaffinity-arxiv-97d6e3ae: abstract, p.1 ('a semi-supervised deep learning model that unifies recurrent and convolutional neural networks'); §§2.2-2.4; 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: DeepAffinity