| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: AutoTPD+ |
| tags: |
| - targeted-protein-degradation |
| - protac |
| - molecular-glue |
| - drug-discovery |
| - tabular |
| arxiv: 2605.11221 |
| configs: |
| - config_name: autotpdplus-protac |
| data_files: |
| - split: train |
| path: AutoTPDplus-PROTAC.csv |
| - config_name: autotpdplus-mg |
| data_files: |
| - split: train |
| path: AutoTPDplus-MG.csv |
| - config_name: autotpd-protac |
| data_files: |
| - split: train |
| path: AutoTPD-PROTAC.csv |
| - config_name: autotpd-mg |
| data_files: |
| - split: train |
| path: AutoTPD-MG.csv |
| --- |
| |
| # AutoTPD+ |
|
|
| [](https://yaochenr.github.io/LLM-TPD-Extraction/) |
| [](https://github.com/yaochenr/LLM-TPD-Extraction) |
| [](https://arxiv.org/abs/2605.11221) |
| [](https://creativecommons.org/licenses/by/4.0/) |
|
|
| AutoTPD+ is a living database of PROTAC and molecular glue degradation assays mined from the literature using an agentic LLM workflow. |
|
|
| It contains standardized compound, target, recruiter, assay, cell-line, DC50, and Dmax information. AutoTPD+ combines literature extracted records with PROTAC-DB 3.0 and MolGlueDB and is designed for monthly updates. |
|
|
| - **7,622** assay records |
| - **1,008** source publications |
| - **2,408** unique chemical structures |
| - **2 modalities:** PROTAC and molecular glue |
|
|
| ## Files |
|
|
| | File | Description | Records | |
| |---|---|---:| |
| | `AutoTPDplus-PROTAC.csv` | Living PROTAC dataset | 6,766 | |
| | `AutoTPDplus-MG.csv` | Living molecular glue dataset | 856 | |
| | `AutoTPD-PROTAC.csv` | Frozen PROTAC benchmark | 714 | |
| | `AutoTPD-MG.csv` | Frozen molecular glue benchmark | 246 | |
|
|
| ## Data format |
|
|
| Each row represents one degradation assay record. |
|
|
| <details> |
| <summary><strong>All columns</strong></summary> |
|
|
| | **Column** | **Description** | |
| | :--- | :--- | |
| | **IDENTIFIERS** | | |
| | `AutoTPD_ID` | Stable row identifier, unchanged across monthly releases | |
| | `Record_ID` | Record ID in the source database; empty for LLM-extracted records | |
| | `Compound_ID` | Compound ID in the source database; empty for LLM-extracted records | |
| | `DOI` | Source publication | |
| | **COMPOUND** | | |
| | `Compound_Name` | Compound name or label as reported in the paper | |
| | `IUPAC_Name` | IUPAC name | |
| | `IUPAC_Name_Source` | Where the IUPAC name came from (PAPER, PUBCHEM) | |
| | `SMILES` | RDKit canonical SMILES | |
| | `SMILES_Source` | Where the SMILES came from (PAPER, PUBCHEM, OPSIN, source database) | |
| | `StdInChIKey` | Standard InChIKey computed by RDKit from the standardized canonical SMILES | |
| | `Connectivity_Key` | First block of the InChIKey (connectivity only, ignores stereochemistry) | |
| | **DEGRADATION TARGET** | | |
| | `Degradation_Target` | Target as reported in the paper | |
| | `Degradation_Target_Gene` | HGNC gene symbol | |
| | `Degradation_Target_Uniprot` | UniProt accession | |
| | `Degradation_Target_Uniprot_MutationID` | UniProt variant ID when a specific mutant is targeted | |
| | `Degradation_Target_Sequence` | UniProt protein sequence | |
| | **RECRUITER** | | |
| | `Recruiter` | Recruiter (usually an E3 ligase) as reported | |
| | `Recruiter_Gene` | Gene symbol | |
| | `Recruiter_Uniprot` | UniProt accession | |
| | `Recruiter_Sequence` | UniProt protein sequence | |
| | **ASSAY CONTEXT** | | |
| | `Assay` | Assay as described in the paper | |
| | `Assay_STD` | Standardized assay family | |
| | `Cell_Line` | Cell line as reported, or cell-free | |
| | `Cell_Line_STD` | Cellosaurus recommended name | |
| | `Cell_Line_STD_Accession` | Cellosaurus accession (RRID) | |
| | `Cell_Line_Modification` | Engineered construct, e.g. a HiBiT knock-in | |
| | `Cell_Line_Species` | Species of the cell line | |
| | **DC50** | | |
| | `DC50` | Concentration giving 50% degradation | |
| | `DC50_units` | Units of DC50 | |
| | `DC50_h` | Treatment time in hours | |
| | `DC50_operator` | Qualifier: `<`, `>`, `~`, `<=`, `>=` | |
| | `DC50_range_min` | Range low end, or mean − error | |
| | `DC50_range_max` | Range high end, or mean + error | |
| | **DMAX** | | |
| | `Dmax` | Maximal degradation, in % | |
| | `Dmax_h` | Treatment time in hours | |
| | `Dmax_operator` | Qualifier: `<`, `>`, `~`, `<=`, `>=` | |
| | `Dmax_range_min` | Range low end, or mean − error | |
| | `Dmax_range_max` | Range high end, or mean + error | |
| | **DMAX CONCENTRATION** | | |
| | `Dmax_conc` | Concentration at which Dmax was measured | |
| | `Dmax_conc_units` | Units of Dmax_conc | |
| | `Dmax_conc_operator` | Qualifier for Dmax_conc | |
| | `Dmax_conc_range_min` | Range low end | |
| | `Dmax_conc_range_max` | Range high end | |
|
|
| </details> |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| protac = load_dataset( |
| "ailab-bio/AutoTPDplus", |
| "autotpdplus-protac", |
| token=True, |
| ) |
| |
| molecular_glue = load_dataset( |
| "ailab-bio/AutoTPDplus", |
| "autotpdplus-mg", |
| token=True, |
| ) |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{rao2026agenticliteratureextractionaugments, |
| title={Agentic Literature Extraction Augments Targeted Protein Degradation Databases}, |
| author={Yaochen Rao and Farzaneh Jalalypour and N. M. Anoop Krishnan and Rocío Mercado}, |
| year={2026}, |
| eprint={2605.11221}, |
| archivePrefix={arXiv}, |
| primaryClass={q-bio.QM}, |
| url={https://arxiv.org/abs/2605.11221}, |
| } |
| ``` |
|
|
| ## License |
|
|
| The structured datasets are released under CC BY 4.0. Original publications, PROTAC-DB, MolGlueDB, and other upstream resources remain subject to their respective licenses. |
|
|
