TACK: A statistical evaluation of degradation activity on a novel TArgeting Chimeras Knowledge dataset

Overview

Proteolysis-targeting chimeras (PROTACs) are bifunctional molecules that recruit an E3 ubiquitin ligase to a target protein of interest (POI), directing it for proteasomal degradation. Predicting the extent and potency of PROTAC-induced degradation, quantified as D_max (maximal degradation) and DC₅₀ (concentration at half-maximal degradation) is a key challenge in targeted protein degradation drug design.

To address this, we developed TACK (TArgeting Chimeras Knowledge), a statistical machine learning framework trained and evaluated on the largest publicly available curated dataset of PROTAC degradation measurements. TACK integrates data from three sources, PROTAC-DB, PROTACpedia, and TPDdb, and trains MLP and XGBoost models under a rigorous repeated 5×5 cross-validation scheme. Ensemble models are constructed via Caruana's greedy forward selection method.

This repository contains the pre-trained ensemble models and ensemble weight specifications required to reproduce the results reported in:

"TACK: A statistical evaluation of degradation activity on a novel TArgeting Chimeras Knowledge dataset" by Stefano Ribes, Nils Dunlop, and Rocío Mercado

The curated dataset is available on Hugging Face at: https://huggingface.co/datasets/ailab-bio/TACK

The accompanying code is available at: https://github.com/ribesstefano/TACK

Data Available

• Ensemble Models: The trained models as checkpoint files, organized in directories for ensemble prediction of degradation activity (bin), D_max, and DC₅₀.
• Prediction Values: The evaluation results from running the trained models.
• Cache: Additional data to reproduce training and run the ensemble models.

File Descriptions

Checkpoints of ensemble models:

• bin_best_arch_ensemble: Ensemble of 25 models of the best architecture for predicting binary degradation activity.
• bin_caruana_ensemble: Ensemble of 18 models selected via Caruana selection for predicting binary degradation activity.
• dmax_best_arch_ensemble: Ensemble of 25 models of the best architecture for predicting maximum degradation activity (D_max).
• dmax_caruana_ensemble: Ensemble of 33 models selected via Caruana selection for predicting maximum degradation activity (D_max).
• dc50_best_arch_ensemble: Ensemble of 25 models of the best architecture for predicting DC50.
• dc50_caruana_ensemble: Ensemble of 22 models selected via Caruana selection for predicting DC50.

Evaluation results:

• predictions/: Contains the prediction files of all 5×5 trained models. The predictions are on the models’ respective validation fold and the common hold-out set. The predictions are per sample and stored as CSV files.
• predictions_protac_stan/: Contains the checkpoints and binary predictions of the PROTAC-STAN model trained on the TACK dataset.

Cached data generated during training:

• cell2cell_id.json: Mapping from cell line names to unique cell IDs in CelloSaurus.
• cell2description.json: Mapping from cell line names to their aggregated textual descriptions (e.g., tissue of origin, disease state).
• cell2data.json: Mapping from cell line names to information from CelloSaurus, stored in JSON format.
• cell_embeddings_model=sentence-transformer_pooling=sum.npz: Precomputed cell line embeddings using a sentence transformer model. The file contains a mapping from cell line IDs to their corresponding embedding vectors.
• morgan_fp_radius16_size512.npz: Mapping from SMILES strings to their corresponding Morgan fingerprints with radius 16 and size 512.
• rdkit_descriptors.npz: Mapping from SMILES strings to their corresponding RDKit descriptors (of size 217).

Original PROTAC data used for data curation can be obtained from:

• PROTAC-DB-v3: http://cadd.zju.edu.cn/protacdb/.
• PROTACpedia: https://protacpedia.weizmann.ac.il/ptcb/main.
• TPDdb: https://tpddb.idrblab.net.

License

This repository is open-source and available under the MIT License.

Contact Information

For questions or feedback, please contact Rocío Mercado: rocio.mercado@chalmers.se.