Benchmarking of PROTAC docking and virtual screening tools
Bibliographic record
Abstract
Abstract Proteolysis targeting chimeras (PROTACs) are bifunctional compounds that recruit an E3 ligase to a target protein to induce ubiquitination and degradation of the target and are pioneer molecules in the field of proximity pharmacology. Rational PROTAC design is a challenging process and novel computational tools have emerged that attempt to predict the ternary complexes created by PROTACs and identify PROTAC candidates. To compare the performance of recent PROTAC design and screening methods, a benchmark was developed to test the ability of these tools to 1) predict the ternary complexes observed in crystal structures and 2) dissociate active from inactive PROTACs. Unlike traditional protein-protein complex prediction software, the PROTAC virtual screening methods often generate successfully PROTAC-induced protein complex structures observed crystallographically, but these experimentally validated predictions are not dissociated from dozens or more of other predicted structures. PROTAC virtual screening efficiency is unclear and highly variable, in part due to the limited size of experimental datasets and the low number of negative controls. Defining ubiquitination zones within cullin-RING complexes does not improve predictions, but conformational arrangements can sometimes be found that are exclusively associated with active PROTACs. Computer assisted PROTAC design is still in its infancy. Pioneering tools highlight the promises and challenges in the field and may be more valuable when guided by clear structural and biophysical data and validated on specific chemical series.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".