README.md

metadata

pretty_name: PockLigGPT Training Data
tags:
  - chemistry
  - drug-discovery
  - molecular-generation
  - protein-ligand
  - crossdocked

PockLigGPT Training Data

Preprocessed training assets for PockLigGPT.

Files

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

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:

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:

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.