P4ward: an automated modelling platform for Protac ternary complexes
Bibliographic record
Abstract
Abstract Proteolysis Targeting Chimeras (Protacs) are a new class of drugs which promote degradation of a protein of interest (POI) by hijacking the Ubiquitin-Proteasome system. Struc tural knowledge of an E3 ligase: Protac:POI ternary complex is required for Protac rational design, and computational modelling of such heteromeric complex structures is nontrivial. To date, few programs have been developed to address this challenge, however, there remains a need for readily accessible tools that can significantly improve ternary complex modelling accuracy. Particularly, programs that can also support the screening phase of Protac discovery, where speed and the ability to test multiple Protacs is essential to advance the field of Protac therapeutics. To bridge these gaps, we present P4ward, a free and fully automated Protac ternary complex modelling pipeline. P4ward achieves a hit-rate of 76.5% with an average rank of 7.26, and substantially reduces the rank of the near-native pose by 73-98% compared to earlier programs. We believe that P4ward could be a user-friendly, fast, and effective tool for gaining atomistic insights necessary for Protac modelling and optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".