biomolecular-modeling.
4 of 378 NOTE documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| NOTE-252 | RNA-FM, and the control that does not carry the claim | Read to open the biological-sequence trunk, which this record did not hold. The structural results are large and the paper’s one clean input-swap control is not: replacing one-hot sequence with a 640-dimensional pretrained embedding in a published UTR model moves R² from 0.814 to 0.816 on the real-human set, where a 16-dimensional predicted secondary structure moves it to 0.820. No practice filed. | Read |
| NOTE-320 | AlphaFold 3 | AlphaFold generalized to all biomolecular complexes, with a diffusion head over atom coordinates. It gains on ligands, nucleic acids and antibodies, and inherits generative failure modes (hallucination, chirality) that it patches with distillation and ranking. Main text read. Extended Data and supplementary methods not read. | Read |
| NOTE-321 | AlphaFold 2 | Near-experimental protein structure accuracy at CASP14, a calibrated confidence head, self-distillation on confident predictions, and a hard dependence on alignment depth below about 30 sequences. Main text and Methods read. The supplementary methods (architecture details, ablation descriptions) were not read. | Read |
| NOTE-360 | MDLM | Masked diffusion’s ELBO is a schedule-weighted average of masked-LM losses, and with a modern recipe it is the best discrete diffusion language model at 110M parameters. It does not beat autoregression in-domain, even with the AR baseline given half the optimizer steps; the paper’s own checklist names that as its limitation. | Read |