Datasets:

gschottlender
/

reverse_ligq

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
name: string
file: string
dtype: string
dim: int64
radius: int64
packed_bits: bool
packed_dim: null
n_ligands: int64
model_id: string
pooling: string
vs
name: string
file: string
dtype: string
dim: int64
radius: int64
packed_bits: bool
packed_dim: int64
n_ligands: int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              name: string
              file: string
              dtype: string
              dim: int64
              radius: int64
              packed_bits: bool
              packed_dim: null
              n_ligands: int64
              model_id: string
              pooling: string
              vs
              name: string
              file: string
              dtype: string
              dim: int64
              radius: int64
              packed_bits: bool
              packed_dim: int64
              n_ligands: int64

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ReverseLigQ dataset (Hugging Face)

This repository contains the ReverseLigQ dataset files in a simplified layout designed to be loaded with a LigandStore/Representation interface (compound representations) plus organism-level auxiliary tables (ReverseLigQ metadata).

Directory layout

compound_data/
  pdb_chembl/
    ligands.parquet
    reps/
      chemberta_zinc_base_768.dat
      chemberta_zinc_base_768.meta.json
      morgan_1024_r2.dat
      morgan_1024_r2.meta.json

rev_ligq/
  fam_prot_dict.pkl
  ligand_lists.pkl
  ligs_fams_curated.pkl
  ligs_fams_possible.pkl
  prot_descriptions.pkl

Compound data (`compound_data/pdb_chembl/`)

`ligands.parquet`

Canonical ligand index table with a dense integer index (lig_idx) used to align all representations on disk.

Typical columns:

chem_comp_id: unified ligand ID (PDB CCD or ChEMBL)
smiles: canonical SMILES
inchikey: optional (may be missing)
lig_idx: dense index 0..N-1 (row order for the .dat matrices)

Representations (`reps/`)

Each representation is stored as:

<rep_name>.dat: memory-mapped matrix on disk
<rep_name>.meta.json: metadata (dtype, dim, packed_bits, etc.)

Available representations:

chemberta_zinc_base_768: ChemBERTa embeddings (dim=768), dense float matrix.
morgan_1024_r2: Morgan fingerprints (1024 bits, radius=2), stored with packed_bits=true.

Organism-specific tables (`rev_ligq/`)

These files provide organism-level ligand lists, Pfam-based protein families, and optional protein descriptions used to project ligand-level similarity hits into candidate protein targets.

ligand_lists.pkl: dict {organism_key (str): [chem_comp_id, ...]}
ligs_fams_curated.pkl: dict {chem_comp_id: [pfam_id, ...]} (curated evidence)
ligs_fams_possible.pkl: dict {chem_comp_id: [pfam_id, ...]} (possible/uncurated evidence)
fam_prot_dict.pkl: nested dict {organism_key: {pfam_id: [uniprot_id, ...]}}
prot_descriptions.pkl: protein descriptions (when available)

Organism keys

ReverseLigQ integrates multiple organisms, each identified by an integer key:

Key	Organism
1	Bartonella bacilliformis
2	Klebsiella pneumoniae
3	Mycobacterium tuberculosis
4	Trypanosoma cruzi
5	Staphylococcus aureus RF122
6	Streptococcus uberis 0140J
7	Enterococcus faecium
8	Escherichia coli MG1655
9	Streptococcus agalactiae NEM316
10	Pseudomonas syringae
11	DENV (Dengue virus)
12	SARS-CoV-2
13	Homo sapiens

Citation

If you use these datasets, please cite:

Schottlender G, Prieto JM, Palumbo MC, Castello FA, Serral F, Sosa EJ, Turjanski AG, Martí MA and Fernández Do Porto D (2022). From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale. Front. Drug. Discov. 2:969983. doi: 10.3389/fddsv.2022.969983

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ReverseLigQ dataset (Hugging Face)

Directory layout

Compound data (compound_data/pdb_chembl/)

ligands.parquet

Representations (reps/)