Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Biomolecular modeling — models whose data is molecules — protein and nucleic-acid structure prediction, biological sequence language models, docking and molecular property prediction — what they get right, what their benchmarks can show, and which of their methods travel to other domains.
3 of 449 SOTA documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| SOTA-352 | When a structure predictor is made generative, add a regression model’s predictions to the training data so disordered regions are learned as disorder rather than invented structure | Abramson et al. (2024), LIT-584 — a diffusion head generates plausible compact structure even where the protein has none. AlphaFold- Multimer, a regression model, renders the same regions as extended loops. Mixing its predictions into training teaches the generative model that convention, and “greatly reduced” hallucination. Remaining hallucinations are flagged by low confidence but do not look disordered. | Proposed |
| SOTA-353 | Train a head that predicts the model’s own accuracy on each output, against the metric you care about, and use it to rank samples and filter predictions | Jumper et al. (2021), LIT-583 — regress the per-residue lDDT the prediction will score (pLDDT) and the predicted TM-score. They track truth at r = 0.76 and 0.85 over 10,795 chains, and pLDDT filters the self-distillation set. Abramson et al. (2024), LIT-584, keep the head under diffusion by rolling out a cheap sample during training. They rank seeds by it, and antibody-interface quality keeps rising up to 1,000 seeds. | Proposed |
| SOTA-354 | When unlabelled inputs vastly outnumber labelled ones, retrain from scratch on the labelled data mixed with the model’s own high-confidence predictions for unlabelled inputs, under augmentation that stops it copying them | Jumper et al. (2021), LIT-583 — about 100,000 solved structures and billions of sequences. AlphaFold 2 predicted structures for about 350,000 unlabeled sequences, kept the confident ones, and retrained the same architecture from scratch on those plus the PDB. Cropping and alignment subsampling mean the student cannot just reproduce the teacher. The ablation shows a clear gain. The filter is the model’s own calibrated confidence (SOTA-353). | Proposed |