De Novo Design of Cyclic Peptide Binders Using Generative Models
Bibliographic record
Abstract
Many natural product therapeutics are cyclic peptides, which have numerous desirable properties as scaffolds for drug discovery. Most therapeutic cyclic peptides, however, contain non-canonical amino acids (ncAAs), which are not modeled in cyclic peptide design models. Given advances in protein structure prediction, I hypothesize that I can modify an AlphaFold3-based model (AF3) to generate candidate cyclic peptide binder designs against a desired target. With Xuezhi Xie, who implemented an optimized cyclic offset encoding on top of Boltz1 (AF3), we developed CyclicBoltz1 to address this gap by enabling the computational design and structure prediction of cyclic peptides containing ncAAs. In this thesis, I evaluate the performance of this model and implement Markov Chain Monte Carlo (MCMC) optimization for binder design. I benchmark the structure prediction and binder design and show that the model achieves more accurate structure prediction in cyclic peptides with ncAAs and stronger binder designs based on DockQ and RMSD scores.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".