A comprehensive primer and review of PROTACs and their In Silico design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Prior protein-protein docking greatly increases the success of structure-based design • The Rosetta suite excels among structure-based ternary complex prediction methods • Lysine density in ubiquitination zone reliably predicts degradation efficiency • Deep learning can model ligand-dependent multicomponent assemblies’ conformations • AlphaFold and RosettaFold trained on experimental data can reshape PROTAC design The cutting-edge technique of Proteolysis Targeting Chimeras, or PROTACs, has gained significant attention as a viable approach for specific protein degradation. This innovative technology has vast potential in fields such as cancer therapy and drug development. The development of effective and specific therapies for a range of diseases is within reach with PROTACs, which can target previously "undruggable" proteins while circumventing the off-target effects of conventional small molecule inhibitors. This manuscript aims to discuss the application of in silico techniques to the design of these groundbreaking molecules and develop PROTAC complexes, in order to identify potential PROTAC candidates with favorable drug-like properties. Additionally, this manuscript reviews the strengths and weaknesses of these methods to demonstrate their utility and highlights the challenges and future prospects of in silico PROTAC design. The present review provides a valuable and beginner-friendly resource for researchers and drug developers interested in using in silico methods for PROTAC design, specifically ternary structure prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it