BiologyarXiv
Heuristic editor, no API keyVerdict: NotableRetroTAC: Learning Route-Derived Synthetic Accessibility for PROTACs
Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation, expanding therapeutic opportunities beyond conventional inhibition.
Key numbers
- 98.75% versus 51.00% are fully
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation, expanding therapeutic opportunities beyond conventional inhibition. However, assembling their large, multifunctional structures creates synthetic challenges that hinder their discovery. Traditional synthetic-accessibility scores can overestimate this difficulty by penalizing PROTACs' molecular complexity while overlooking modular assembly from accessible building blocks. In contrast, retrosynthesis planning tools provide explicit routes but are computationally expensive, particularly for large molecules. Here, we introduce a human-interpretable, PROTAC-oriented route-derived score that rewards short synthetic depth and balanced joining of molecular fragments, and RetroTAC, a machine-learning surrogate that predicts this score directly from molecular structures. RetroTAC combines complementary models in an ensemble, achieving a root mean squared error of 0.13 and an R² of 0.65 on held-out molecules. We further evaluate its utility on PROTACs enumerated by recombining warheads, linkers, and E3-ligase ligands, followed by retrosynthetic planning and route analysis. Candidates selected for high predicted scores achieve a mean route-derived synthesizability of 0.73, compared with 0.31 for low-score candidates; 98.75% versus 51.00% are fully resolved under the tested search settings. These results support RetroTAC as a fast prioritization tool for virtual PROTAC libraries, connecting molecular screening with interpretable route characteristics while reserving explicit planning for selected candidates.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 24% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 16% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ███░░ 3 | 20% | Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data. |
| Novelty | ███░░ 3 | 20% | A genuinely new approach to an open problem. |
| Trajectory | ██░░░ 2 | 10% | Some room to improve with obvious engineering. |
| Stakes | ██░░░ 2 | 10% | Benefits a professional community (practitioners, clinicians, engineers). |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (versus baseline); novelty (discovery); verification (held-out test, code released); stakes (prevention or cure).
How the score was computed
- Merit
- 5.6 / 10
- Adjusted merit
- 4.6 / 10
- Attention
- 0%
- Freshness
- 77%