--- dataset_info: - config_name: DC50 features: - name: SMILES dtype: string - name: POI_Name dtype: string - name: POI_Sequence dtype: string - name: Ligase_Name dtype: string - name: Cell_Line dtype: string - name: Value_Type dtype: string - name: Value_Unit dtype: string - name: Value dtype: float64 - name: Value_Error dtype: float64 - name: Value_Range_Min dtype: float64 - name: Value_Range_Max dtype: float64 - name: Value_Concentration dtype: float64 - name: Assay_Time dtype: float64 - name: Value_Operator dtype: string - name: Value_Category dtype: string - name: Value_Concentration_Unit dtype: string - name: Modality dtype: string - name: POI_UniProt dtype: string - name: Ligase_UniProt dtype: string - name: Ligase_Sequence dtype: string - name: Cell_Line_ID dtype: string - name: Cell_Line_Species dtype: string - name: Reference dtype: string - name: Description dtype: string - name: Database dtype: string - name: Assay dtype: string - name: TPD_ID dtype: string - name: Cell_Line_Description dtype: string - name: POI_Cluster dtype: int64 - name: SMILES_Held_Out dtype: bool - name: SMILES_Scaffold_Cluster dtype: int64 - name: SMILES_Butina_Cluster dtype: int64 splits: - name: train num_bytes: 15459109 num_examples: 4184 download_size: 618282 dataset_size: 15459109 - config_name: Dmax features: - name: SMILES dtype: string - name: POI_Name dtype: string - name: POI_Sequence dtype: string - name: Ligase_Name dtype: string - name: Cell_Line dtype: string - name: Value_Type dtype: string - name: Value_Unit dtype: string - name: Value dtype: float64 - name: Value_Error dtype: float64 - name: Value_Range_Min dtype: float64 - name: Value_Range_Max dtype: float64 - name: Value_Concentration dtype: float64 - name: Assay_Time dtype: float64 - name: Value_Operator dtype: string - name: Value_Category dtype: string - name: Value_Concentration_Unit dtype: string - name: Modality dtype: string - name: POI_UniProt dtype: string - name: Ligase_UniProt dtype: string - name: Ligase_Sequence dtype: string - name: Cell_Line_ID dtype: string - name: Cell_Line_Species dtype: string - name: Reference dtype: string - name: Description dtype: string - name: Database dtype: string - name: Assay dtype: string - name: TPD_ID dtype: string - name: Cell_Line_Description dtype: string - name: POI_Cluster dtype: int64 - name: SMILES_Held_Out dtype: bool - name: SMILES_Scaffold_Cluster dtype: int64 - name: SMILES_Butina_Cluster dtype: int64 splits: - name: train num_bytes: 8580261 num_examples: 2377 download_size: 338826 dataset_size: 8580261 - config_name: default features: - name: SMILES dtype: string - name: POI_Name dtype: string - name: POI_Sequence dtype: string - name: Ligase_Name dtype: string - name: Cell_Line dtype: string - name: Value_Type dtype: string - name: Value_Unit dtype: string - name: Value dtype: float64 - name: Value_Error dtype: float64 - name: Value_Range_Min dtype: float64 - name: Value_Range_Max dtype: float64 - name: Value_Concentration dtype: float64 - name: Assay_Time dtype: float64 - name: Value_Operator dtype: string - name: Value_Category dtype: string - name: Value_Concentration_Unit dtype: string - name: Modality dtype: string - name: POI_UniProt dtype: string - name: Ligase_UniProt dtype: string - name: Ligase_Sequence dtype: string - name: Cell_Line_ID dtype: string - name: Cell_Line_Species dtype: string - name: Reference dtype: string - name: Description dtype: string - name: Database dtype: string - name: Assay dtype: string - name: TPD_ID dtype: string - name: Cell_Line_Description dtype: string - name: POI_Cluster dtype: int64 - name: SMILES_Held_Out dtype: bool - name: SMILES_Scaffold_Cluster dtype: int64 - name: SMILES_Butina_Cluster dtype: int64 splits: - name: train num_bytes: 24039370 num_examples: 6561 download_size: 798178 dataset_size: 24039370 - config_name: multitask features: - name: SMILES dtype: string - name: POI_Name dtype: string - name: POI_Sequence dtype: string - name: Ligase_Name dtype: string - name: Cell_Line dtype: string - name: Value_Type_DC50 dtype: string - name: Value_Unit_DC50 dtype: string - name: Value_DC50 dtype: float64 - name: Value_Error_DC50 dtype: float64 - name: Value_Range_Min_DC50 dtype: float64 - name: Value_Range_Max_DC50 dtype: float64 - name: Value_Concentration_DC50 dtype: float64 - name: Assay_Time dtype: float64 - name: Value_Operator_DC50 dtype: string - name: Value_Category_DC50 dtype: string - name: Value_Concentration_Unit_DC50 dtype: 'null' - name: Modality dtype: string - name: POI_UniProt dtype: string - name: Ligase_UniProt dtype: string - name: Ligase_Sequence dtype: string - name: Cell_Line_ID dtype: string - name: Cell_Line_Species dtype: string - name: Reference dtype: string - name: Description dtype: string - name: Database dtype: string - name: Assay dtype: string - name: TPD_ID_DC50 dtype: string - name: Cell_Line_Description dtype: string - name: POI_Cluster dtype: int64 - name: SMILES_Held_Out dtype: bool - name: SMILES_Scaffold_Cluster dtype: int64 - name: SMILES_Butina_Cluster dtype: int64 - name: Value_Type_Dmax dtype: string - name: Value_Unit_Dmax dtype: string - name: Value_Dmax dtype: float64 - name: Value_Error_Dmax dtype: float64 - name: Value_Range_Min_Dmax dtype: float64 - name: Value_Range_Max_Dmax dtype: float64 - name: Value_Concentration_Dmax dtype: float64 - name: Value_Operator_Dmax dtype: string - name: Value_Category_Dmax dtype: string - name: Value_Concentration_Unit_Dmax dtype: string - name: TPD_ID_Dmax dtype: string splits: - name: train num_bytes: 6303033 num_examples: 1563 download_size: 261333 dataset_size: 6303033 configs: - config_name: DC50 data_files: - split: train path: DC50/train-* - config_name: Dmax data_files: - split: train path: Dmax/train-* - config_name: default data_files: - split: train path: data/train-* - config_name: multitask data_files: - split: train path: multitask/train-* license: mit tags: - tabular pretty_name: TACK --- # TACK — TArgeting Chimeras Knowledge **A curated, ML-ready dataset and benchmark for PROTAC degradation activity prediction** [![Paper](https://img.shields.io/badge/Paper-KDD%202026-blue)](https://arxiv.org/abs/2605.19579) [![GitHub](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/ribesstefano/TACK) [![Models](https://img.shields.io/badge/Models-Zenodo-green)](https://zenodo.org/uploads/15691822) [![License](https://img.shields.io/badge/License-MIT-yellow)](LICENSE) --- ## Overview Proteolysis-targeting chimeras (PROTACs) are a promising drug modality that induces targeted protein degradation by hijacking the cell's native ubiquitin–proteasome system. However, rational PROTAC design remains challenging due to the complex interplay between molecular structure, target proteins (POIs), E3 ligases, and cellular context. **TACK** addresses three critical gaps in existing PROTAC ML benchmarks: - **Data scarcity and inconsistency** — harmonized from three major public repositories with standardized SMILES, protein annotations, and experimental conditions - **Lack of rigorous benchmarks** — scaffold-based 5×5 cross-validation and formal statistical testing (Friedman, Wilcoxon, Tukey HSD) rather than simple train/test splits - **Limited scope** — supports regression (*pDC₅₀*, *D*max) and binary classification, not just the binary classification framing common in prior work > Ribes\*, Dunlop\*, and Mercado. *TACK: A statistical evaluation of degradation activity on a novel TArgeting Chimeras Knowledge dataset.* KDD AI for Sciences Track, 2026. --- ## Dataset at a Glance | Statistic | TPDdb | PROTAC-DB | PROTACpedia | **TACK** | |---|---|---|---|---| | Total records | 22,183 | 9,380 | 1,203 | **6,561** | | Unique PROTACs | 21,429 | 6,110 | 1,189 | **3,514** | | Degradation endpoints | 6,518 | 2,170 | 580 | **6,561** | | POI targets | 184 | 441 | 79 | **164** | | E3 ligases | 8 | 21 | 8 | **9** | | Cell lines | 142 | — | 139 | **155** | | Hold-out set entries | — | — | — | **913** | The dataset contains **4,184 DC₅₀** and **2,377 D**max measurements. Approximately 55% of entries are classified as active (DC₅₀ ≤ 100 nM **and** D*max* ≥ 80%). **Most represented biology:** - **POIs:** Androgen Receptor (26.7%), SMARCA2 (12.0%), BTK (8.9%) — top 5 account for 61.6% of endpoints - **E3 ligases:** CRBN and VHL together account for 98.7% of measurements - **Cell lines:** LNCaP (20.9%), SW1573 (15.2%), Mino (7.0%) across 155 unique lines --- ## Configs (Subsets) | Config | Description | Examples | |---|---|---| | `default` | All endpoints (DC₅₀ + D*max* combined) | 6,561 | | `DC50` | Potency measurements only | 4,184 | | `Dmax` | Maximal degradation efficacy only | 2,377 | | `multitask` | Paired DC₅₀ + D*max* for the same PROTAC/assay, also used for binary activity classification | 1,563 | --- ## Loading the Dataset ```python from datasets import load_dataset # All endpoints ds = load_dataset("ailab-bio/TACK") # DC50 only ds_dc50 = load_dataset("ailab-bio/TACK", "DC50") # Dmax only ds_dmax = load_dataset("ailab-bio/TACK", "Dmax") # Paired multitask (DC50 + Dmax for same compound) ds_multi = load_dataset("ailab-bio/TACK", "multitask") ``` The `SMILES_Held_Out` column flags the structurally dissimilar held-out set (~10% of data). To reproduce the train/validation split used in the paper: ```python ds = load_dataset("ailab-bio/TACK", "DC50")["train"] train_val = ds.filter(lambda x: not x["SMILES_Held_Out"]) held_out = ds.filter(lambda x: x["SMILES_Held_Out"]) ``` --- ## Schema ### Common columns (all configs) | Column | Type | Description | |---|---|---| | `SMILES` | string | Canonical RDKit SMILES of the PROTAC | | `POI_Name` | string | Gene name of the protein of interest | | `POI_Sequence` | string | UniProt amino acid sequence of the POI | | `POI_UniProt` | string | UniProt accession for the POI | | `Ligase_Name` | string | Name of the E3 ligase | | `Ligase_Sequence` | string | UniProt amino acid sequence of the E3 ligase | | `Ligase_UniProt` | string | UniProt accession for the E3 ligase | | `Cell_Line` | string | Cell line name (Cellosaurus-standardized) | | `Cell_Line_ID` | string | Cellosaurus identifier | | `Cell_Line_Species` | string | Species of origin for the cell line | | `Cell_Line_Description` | string | Textual description from Cellosaurus | | `Value` | float64 | Measured endpoint value | | `Value_Unit` | string | Unit of the value (nM for DC₅₀; % for D*max*) | | `Value_Type` | string | Endpoint type (DC50 or Dmax) | | `Value_Operator` | string | Comparison operator if censored (`<`, `>`, etc.) | | `Value_Error` | float64 | Reported measurement error (if available) | | `Value_Range_Min` | float64 | Lower bound for range-reported values | | `Value_Range_Max` | float64 | Upper bound for range-reported values | | `Value_Concentration` | float64 | Treatment concentration for D*max* assays | | `Value_Concentration_Unit` | string | Unit of treatment concentration | | `Value_Category` | string | Categorical activity label (where applicable) | | `Assay_Time` | float64 | Treatment duration in hours | | `Assay` | string | Standardized assay type (e.g., Western Blot, HiBit) | | `Modality` | string | Compound modality (PROTAC, molecular glue, etc.) | | `Reference` | string | Source literature or patent reference | | `Description` | string | Original assay description | | `Database` | string | Source database (TPDdb, PROTAC-DB, PROTACpedia) | | `TPD_ID` | string | Original identifier in the source database | | `POI_Cluster` | int64 | POI sequence-based cluster ID | | `SMILES_Scaffold_Cluster` | int64 | Murcko scaffold cluster ID (used for CV splitting) | | `SMILES_Butina_Cluster` | int64 | Butina/Tanimoto cluster ID | | `SMILES_Held_Out` | bool | **True** if this entry belongs to the structural hold-out set | The `multitask` config replaces `Value`, `Value_Unit`, etc. with `Value_DC50`, `Value_Dmax` and analogous suffixed columns, enabling simultaneous supervision on both endpoints. --- ## Curation Pipeline Raw data was aggregated from three repositories and cleaned through a multi-stage pipeline: 1. **SMILES standardization** — canonicalization via RDKit; duplicates resolved by weighted scoring 2. **Endpoint standardization** — DC₅₀ values converted to nM; range values converted to arithmetic mean with bounds stored; censored values (operators) preserved but flagged for exclusion from evaluation sets; categorical patent grades excluded 3. **Protein annotation** — POI and E3 ligase names mapped to UniProt accessions; amino acid sequences retrieved from UniProt; BRD4 isoform handling applied 4. **Cell line standardization** — validated against Cellosaurus; standardized identifiers and descriptions attached 5. **Assay standardization** — assay descriptions parsed and normalized (e.g., "WB" → "Western Blot"); treatment concentrations extracted from metadata and free-text fields 6. **Hold-out construction** — ~10% most structurally dissimilar PROTACs (by average Tanimoto distance using 512-bit Morgan8 fingerprints) isolated before any model training 7. **Scaffold-based CV clustering** — remaining data grouped by Murcko scaffolds to prevent leakage between CV folds Please refer to [this link](https://github.com/ribesstefano/TACK/tree/main/tack_dataset) for the codebase used to generate the data. --- ## Benchmark Results Models were evaluated using **scaffold-based 5×5 repeated cross-validation** with statistical testing (Friedman + Wilcoxon + Benjamini–Hochberg correction for feature selection; Tukey HSD for architecture comparison). ### Regression (hold-out set) | Task | Model | MAE | RMSE | R² | Spearman ρ | |---|---|---|---|---|---| | pDC₅₀ | MLP | 0.58 | 0.80 | **0.66** | 0.76 | | D*max* | XGBoost | 18.86 | 25.68 | **0.36** | 0.66 | ### Binary Classification (validation folds, mean ± CI) | Model | ROC-AUC | PR-AUC | MCC | Recall @ Prec≥0.8 | |---|---|---|---|---| | **XGBoost** | **0.851** | **0.870** | **0.523** | **0.777** | | MLP | 0.799 | 0.824 | 0.448 | 0.600 | | PROTAC-STAN | 0.746 | 0.773 | 0.405 | 0.443 | All pairwise differences are statistically significant (Tukey HSD, *p* < 0.001). **Key findings:** - pDC₅₀ is substantially more predictable than D*max* (R² 0.66 vs. 0.36), reflecting the greater dependence of maximal degradation on cellular factors not captured by current molecular representations - Classical tree-based methods (XGBoost) outperform the domain-specific GNN PROTAC-STAN, consistent with the small-to-medium tabular dataset regime - Simple one-hot or n-gram protein encodings often match expensive ESM-S embeddings, particularly with XGBoost; ESM-S embeddings give a modest advantage for XGBoost on the classification task ### Ensemble Uncertainty Quantification Ensemble standard deviation correlates positively with absolute prediction error, enabling confidence-aware compound prioritization: | Task | Method | RMSE | Spearman ρ (σ vs. \|error\|) | |---|---|---|---| | pDC₅₀ | Caruana (33 models) | 0.672 | 0.207* | | pDC₅₀ | Uniform avg (500 models) | 0.683 | 0.355** | | D*max* | Caruana (22 models) | 21.32 | 0.543** | | D*max* | Uniform avg (500 models) | 22.92 | 0.694** | \*\* *p* < 0.001; \* *p* < 0.01 --- ## Best Models Pre-trained ensemble models (XGBoost and MLP, all feature configurations, 25 CV folds each) are available on Zenodo: [![Zenodo](https://img.shields.io/badge/Models-Zenodo%2015691822-blue)](https://zenodo.org/uploads/15691822) --- ## Citation If you use TACK in your research, please cite: ```bibtex @misc{ribes2026tackstatisticalevaluationdegradation, title={TACK: A statistical evaluation of degradation activity on a novel TArgeting Chimeras Knowledge dataset}, author={Stefano Ribes and Nils Dunlop and Rocío Mercado}, year={2026}, eprint={2605.19579}, archivePrefix={arXiv}, primaryClass={q-bio.QM}, url={https://arxiv.org/abs/2605.19579}, } ``` --- ## License The TACK dataset is released under the **MIT License**. The underlying experimental data is sourced from PROTAC-DB, PROTACpedia, and TPDdb — please refer to the respective database licenses for conditions on downstream use of the original data. --- ## Acknowledgements SR and RM acknowledge funding from the Chalmers Gender Initiative for Excellence (Genie). RM and ND acknowledge funding from the Wallenberg AI, Autonomous Systems and Software Program (WASP), supported by the Knut and Alice Wallenberg Foundation. The authors thank Yossra Gharbi, Alexander Persson, and Felix Erngård for helpful discussions. Computations were enabled by Chalmers e-Commons and NAISS (grant 2022-06725).