CyclicBoltz1, fast and accurately predicting structures of cyclic peptides and complexes containing non-canonical amino acids using AlphaFold 3 Framework
Bibliographic record
Abstract
Abstract Cyclic peptides exhibit favourable properties making them promising candidates as therapeutics. While the design and modeling of peptides in general has seen rapid advances since the advent of modern machine learning methods, existing deep learning models cannot effectively predict cyclic peptide structures containing non-canonical amino acids (ncAAs), which are often crucial for peptide therapeutics. To address this limitation, we here extend the recent AF3-style model Boltz to cyclic peptides with ncAAs. In addition to the positional encoding offset, we used a simple yet effective extension of the cyclic offset encoding based on AlphaFold3’s tokenization scheme that allows the modeling of cyclic peptides with modified residues. On a test set of peptides with ncAAs, our approach outperforms HighFold2 on 13/17 cases, with an average C α RMSD of 1.877Å and an average all-atom RMSD of 3.361Å. Our results show that the cyclic offset encoding shown for AlphaFold2 generalizes to AlphaFold3-based models and can be extended to incorporate ncAAs, showing potential in the design of novel cyclic peptides with ncAAs for therapeutic applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".