Nativeness-constrained diffusion framework for nanobody design
Bibliographic record
Abstract
Abstract Aim Nanobodies are single-domain antibodies with promising therapeutic potential, yet accurate complementarity-determining region (CDR) design remains challenging. Methods Existing diffusion-based frameworks such as DiffAb[1] and RFantibody[2] typically generate CDR sequences on fixed scaffolds by enforcing structural and physical constraints, but they neglect evolutionary information intrinsic to antibody repertoires. We propose a scaffold-constrained diffusion framework that integrates a nativeness prior, guiding generation toward sequences that are not only structurally consistent but also evolutionarily plausible. Results By incorporating nativeness-aware constraints during design, our model produces nanobody candidates that better balance physical compatibility with natural repertoire characteristics, achieving improved sequence realism and developability. Conclusion This approach provides a new computational strategy for antibody engineering, enabling more realistic and efficient nanobody design. References 1. Luo S., Su Y., Peng X., Wang S., Peng J., Ma J. ‘Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures.’ Advances in Neural Information Processing Systems 2022. 2. Bennett N.R., Watson J.L., Ragotte R.J., Borst A.J., See D.L., Weidle C., Biswas R., Shrock E.L., Leung P.J.Y., Huang B., Goreshnik I., Ault R., Carr K.D., Singer B., Criswell C., Vafeados D., Garcia Sanchez M., Kim H.M., Vázquez Torres S., Chan S., Baker D. ‘Atomically accurate de novo design of single-domain antibodies.’ bioRxiv 2024.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".