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.
7 of 737 LIT documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| LIT-505 v2 | Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences | Rives et al. (2021), PNAS 118(15) — the protein language model the biological-sequence line runs from. A transformer trained by masked-token prediction on 250 million protein sequences, with no structural or functional labels, produces representations in which secondary structure, tertiary contacts and remote homology are linearly recoverable. Held here as the ancestor of LIT-506, which reimplements the recipe for RNA and copies its downstream head. | Active |
| LIT-506 v2 | Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions | Chen et al. (2022), ARXIV-2204.00300 — a 12-layer BERT encoder trained by masked-token prediction on 23.7 million unannotated ncRNA sequences from RNAcentral, then used as a frozen feature extractor for secondary structure, 3D contact, protein-RNA binding and UTR regulation. Beats twelve secondary-structure methods (F1 0.941 on ArchiveII600, 0.704 on bpRNA TS0). Read as NOTE-252, which records that the paper’s one clean input-swap control moves R² by 0.002 on the generalization set. | Active |
| LIT-583 | Highly accurate protein structure prediction with AlphaFold | Jumper et al. (Nature 2021), DOI 10.1038/s41586-021-03819-2. AlphaFold 2 predicts protein structure from sequence and a multiple sequence alignment. At CASP14 its median backbone error was 0.96 Å r.m.s.d.95, against 2.8 Å for the next best method. Its per-residue confidence, pLDDT, tracks true accuracy (r = 0.76 over 10,795 chains). Self-distillation on its own confident predictions for 350,000 unlabeled sequences improves it. Accuracy falls sharply when the alignment has fewer than about 30 sequences. | Active |
| LIT-584 | Accurate structure prediction of biomolecular interactions with AlphaFold 3 | Abramson et al. (Nature 2024), DOI 10.1038/s41586-024-07487-w. AlphaFold 3 predicts complexes of proteins, nucleic acids, ligands, ions and modified residues. A pairformer with lighter MSA processing replaces the Evoformer, and a diffusion module over raw atom coordinates replaces the structure module, with no built-in rotational equivariance. It beats Vina on PoseBusters without the pocket information Vina gets (P = 2.27×10⁻¹³). The generative head hallucinates structure in disordered regions, which cross-distillation from AlphaFold-Multimer reduces. Chirality is violated in 4.4% of PoseBusters predictions even after ranking penalties. | Active |
| LIT-679 | Dynamic Chunking for End-to-End Hierarchical Sequence Modeling | Hwang, Wang and Gu (2025; ICLR 2026), ARXIV-2507.07955. H-Net is a byte-level hierarchical model that learns where to cut chunks. Compared with a GPT-2 BPE Transformer at matched bytes per batch and FLOPs per byte, a chain of baselines separates each ingredient. One stage of learned chunking only ties BPE and whitespace chunking (0.728 against 0.730 against 0.726 bits/byte at XL). The gain is the second stage (0.715, +2.7 downstream average). The largest gains are on Chinese. It is limited to 1.3B-equivalent FLOPs and trains about 2× slower in wall-clock. | Active |
| LIT-702 | Simple and Effective Masked Diffusion Language Models | Sahoo et al. (2024), ARXIV-2406.07524. MDLM: the masked-diffusion training objective reduces to a weighted average of BERT-style masked-LM losses, a continuous-time ELBO whose value is invariant to the noise schedule. It is the strongest discrete diffusion language model at its scale — beating SEDD at matched tokens — and still trails a retrained autoregressive baseline in-domain (LM1B 27.04 vs 22.32; OWT 23.21 vs 17.54), which the authors state as the limitation. | Active |
| LIT-754 | Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism | Wang et al. (2022), ARXIV-2211.00235. Move the Evoformer’s outer product mean to the end of the block, so the MSA and pair stacks no longer depend on each other within a block; this matches the original block’s accuracy and speed. Then put the two stacks on two GPUs. On a batch of one protein per device, where data parallelism cannot scale further, this cuts end-to-end AlphaFold2 training from 5.8 to 4.2 days on UniFold and from 6.7 to 4.9 days on HelixFold, using twice the GPUs. | Active |