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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- other
+tags:
+- protein-ligand
+- structure-based-drug-design
+- molecular-generation
+- anewomni
+pretty_name: AnewOmni Training Datasets (non-SIU)
+---
+
+# AnewOmni Training Datasets — non-SIU subset
+
+Preprocessed, **training-ready** datasets for [AnewOmni](https://github.com/bytedance/AnewOmni)
+("Programming Biomolecular Interactions with an All-Atom Generative Model"), in the codebase's
+memory-mapped format (`data.bin` + `index.txt` + split/cluster index files).
+
+This bundle holds **7 of the 8** training sources — **everything except SIU** (SIU is ~156 GB and is
+distributed separately). After extraction the `datasets/` tree matches the paths in
+`configs/train_ldm.yaml` / `configs/train_vae.yaml` with **0 missing files**.
+
+## Contents
+
+| Dataset | Modality | Entries | Splits | Original source |
+|---|---|---:|---|---|
+| PepBench | peptide | 6,105 | train 4,157 / valid 114 (LNR 93 test held out) | Zenodo 13373108 (PepGLAD) |
+| ProtFrag | peptide (augmentation) | 70,498 | — | Zenodo 13373108 (PepGLAD) |
+| SAbDab | antibody | 16,947 | train 9,473 / valid 400 | OPIG / UniMoMo |
+| PDBbind v2020 | small molecule | 16,200 | refined 4,628 (+train/valid) + other-PL 11,572 | PDBbind v2020 |
+| BioLiP2-nr | small molecule | 51,114 | pretrain / finetune (by resolution) | zhanggroup.org/BioLiP |
+| CrossDocked2020 | small molecule | 100,081 | train 99,881 / valid 100 / test 100 | TargetDiff Google Drive |
+| _SIU (separate upload)_ | small molecule | 4,289,980 | cluster-weighted | HuggingFace `bgao95/SIU` |
+
+Bundle total: **~261k entries, 17.2 GB** (`anewomni_datasets_noSIU.tar`).
+
+## Usage
+
+```bash
+# on your training server, from the AnewOmni repo root:
+hf download Windsao/anewomni-train-data anewomni_datasets_noSIU.tar --repo-type dataset --local-dir .
+tar -xf anewomni_datasets_noSIU.tar        # -> ./datasets/...
+rm anewomni_datasets_noSIU.tar
+```
+
+The extracted layout:
+
+```
+datasets/
+  peptide/pepbench/processed/        (+ ../train.cluster)
+  peptide/ProtFrag/processed/
+  antibody/SAbDab/processed/
+  molecule_v2/PDBbind/processed/{refined-set,v2020-other-PL}/
+  molecule_v2/biolip2_nr/processed/
+  molecule_v2/CrossDocked/processed/
+```
+
+## Format
+
+Each dataset is a **memory-mapped store**:
+- `data.bin` — concatenated, per-entry **zlib-compressed** complexes (receptor + binder).
+- `index.txt` — one line per entry: `id \t start \t end \t properties-json` (byte range `[start,end)` into `data.bin`).
+- `*_index.txt` — train / valid / test subsets (an entry list).
+- `*.cluster` — sequence / scaffold clusters used for size- and redundancy-aware sampling.
+
+Loadable via the AnewOmni `MoleculeDataset` / `PeptideDataset` / `AntibodyDataset` classes (`mmap_dir=…`, `specify_index=…`, `cluster=…`).
+
+## Notes
+
+- **Counts are from raw processing of each source.** The AnewOmni paper applies an additional
+  ligand-quality filtering pipeline (CCD exclusion list, cross-source dedup by PDB code, 40%
+  sequence-identity train/test cutoff) that is **not** applied here — so the small-molecule counts
+  above are somewhat higher than the paper's final training numbers (PDBbind 14,200 / BioLiP2 18,012 /
+  CrossDocked 85,938). PepBench (4,157) and SAbDab (9,473/400) splits match the paper exactly.
+- Derived from publicly available datasets (mostly CC-BY); refer to each original source for its license.