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README.md

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+---
+pretty_name: PockLigGPT Training Data
+tags:
+  - chemistry
+  - drug-discovery
+  - molecular-generation
+  - protein-ligand
+  - crossdocked
+---
+
+# PockLigGPT Training Data
+
+Preprocessed training assets for
+[PockLigGPT](https://github.com/pablovaras/PockLigGPT_official).
+
+## Files
+
+```text
+chembl/
+├── train_chembl.bin
+└── val_chembl.bin
+
+crossdocked/
+├── crossdocked_clean_pocket_selfies_with_tokens.parquet
+├── per_residue_index.parquet
+└── per_residue_pack.npz.part-*
+
+tokenizer/
+└── meta_chembl_db_aa_2_proto4.pkl
+```
+
+The CrossDocked training Parquet contains pocket metadata, SELFIES,
+fixed-length `token_ids`, and offsets into the residue embedding stack.
+
+The split NPZ contains `emb_stack` with shape `(8_337_780, 1024)` and dtype
+`float16`. Each row is a ProtT5 residue embedding. The index Parquet maps each
+pocket to its `start` and `length` values.
+
+## Download
+
+```bash
+hf download pablovp8/PockLigGPT-training-data \
+  --repo-type dataset \
+  --include "chembl/*" \
+  --include "crossdocked/*" \
+  --local-dir datasets/processed
+
+hf download pablovp8/PockLigGPT-training-data \
+  --repo-type dataset \
+  --include "tokenizer/*" \
+  --local-dir datasets
+```
+
+## Prepare CrossDocked embeddings
+
+Reassemble the downloaded archive:
+
+```bash
+python scripts/assemble_embedding_pack.py \
+  --parts-dir datasets/processed/crossdocked \
+  --output datasets/processed/crossdocked/per_residue_pack.npz \
+  --delete-parts
+```
+
+Then convert it once to an NPY file before finetuning so NumPy can memory-map
+the embedding stack:
+
+```bash
+python scripts/convert_embedding_pack.py \
+  --input datasets/crossdocked/per_residue_pack.npz \
+  --output datasets/crossdocked/per_residue_pack.npy \
+  --delete-source
+```
+
+When downloading directly into the PockLigGPT repository, place the files
+under `datasets/processed/crossdocked/` or update the paths in
+`config/training/finetune_2/crossdocked_sequence_add.yaml`.
+
+## Training stages
+
+- ZINC20 binary files are used for pretraining.
+- ChEMBL binary files are used for finetune 1. They contain 1,061,229
+  training sequences and 117,916 validation sequences with block size 156.
+- CrossDocked Parquet and ProtT5 embeddings are used for finetune 2.
+
+ZINC20 binaries are not distributed here because of their size. Generate them
+from the source dataset with `config/tokenization/zinc20.yaml`.
+
+## Notes
+
+The tokenizer metadata is a Python pickle file. Only load pickle files from
+sources you trust.
+
+Users are responsible for complying with the licenses and terms of the
+upstream molecular and structural datasets.

chembl/train_chembl.bin

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+oid sha256:b5372074fad84a93d2a8ace5604d35747f043e513fb938357e9c7e37132040a1
+size 331103448

chembl/val_chembl.bin

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+oid sha256:2e45806fdbe20697d1005a8f4d775a45c01b115bf829de8eea948fb1c74e05e8
+size 36789792

crossdocked/crossdocked_clean_pocket_selfies_with_tokens.parquet

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+oid sha256:88518156c75561fa59a548177048db936a60d23fcff76333818b34293395999e
+size 31266256

crossdocked/per_residue_index.parquet

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+oid sha256:d946e6c11174e35319872f93bd56158436ce5817ac8a53392358f677ff8c1195
+size 9600814

tokenizer/meta_chembl_db_aa_2_proto4.pkl

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+oid sha256:3d2d45b3f2bf789e5c04edae331d794706a1180134859235f65a993c9ad9782a
+size 8553