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--- |
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license: mit |
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task_categories: |
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- other |
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tags: |
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- bioinformatics |
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- cheminformatics |
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- drug-discovery |
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- virtual-screening |
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- reverse-screening |
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- chemberta |
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- rdkit |
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pretty_name: ReverseLigQ Dataset |
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--- |
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# ReverseLigQ dataset (Hugging Face) |
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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). |
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## Directory layout |
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``` |
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compound_data/ |
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pdb_chembl/ |
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ligands.parquet |
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reps/ |
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chemberta_zinc_base_768.dat |
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chemberta_zinc_base_768.meta.json |
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morgan_1024_r2.dat |
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morgan_1024_r2.meta.json |
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merged_databases/ |
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binding_data_merged.parquet |
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uncurated_binding_data.parquet |
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ligs_smiles_merged.parquet |
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rev_ligq/ |
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fam_prot_dict.pkl |
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ligand_lists.pkl |
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ligs_fams_curated.pkl |
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ligs_fams_possible.pkl |
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prot_descriptions.pkl |
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``` |
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## Compound data (`compound_data/pdb_chembl/`) |
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### `ligands.parquet` |
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Canonical ligand index table with a **dense integer index** (`lig_idx`) used to align all representations on disk. |
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Typical columns: |
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- `chem_comp_id`: unified ligand ID (PDB CCD or ChEMBL) |
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- `smiles`: canonical SMILES |
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- `inchikey`: optional (may be missing) |
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- `lig_idx`: dense index **0..N-1** (row order for the `.dat` matrices) |
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### Representations (`reps/`) |
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Each representation is stored as: |
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- `<rep_name>.dat`: memory-mapped matrix on disk |
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- `<rep_name>.meta.json`: metadata (dtype, dim, packed_bits, etc.) |
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Available representations: |
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- `chemberta_zinc_base_768`: ChemBERTa embeddings (dim=768), dense float matrix. |
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- `morgan_1024_r2`: Morgan fingerprints (1024 bits, radius=2), stored with `packed_bits=true`. |
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## Detailed binding data (merged_databases/) |
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Known binding data from PDB and ChEMBL. |
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## Organism-specific tables (`rev_ligq/`) |
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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. |
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- `ligand_lists.pkl`: dict `{organism_key (str): [chem_comp_id, ...]}` |
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- `ligs_fams_curated.pkl`: dict `{chem_comp_id: [pfam_id, ...]}` (curated evidence) |
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- `ligs_fams_possible.pkl`: dict `{chem_comp_id: [pfam_id, ...]}` (possible/uncurated evidence) |
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- `fam_prot_dict.pkl`: nested dict `{organism_key: {pfam_id: [uniprot_id, ...]}}` |
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- `prot_descriptions.pkl`: protein descriptions (when available) |
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### Organism keys |
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ReverseLigQ integrates multiple organisms, each identified by an integer key: |
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| Key | Organism | |
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|---:|---| |
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| 1 | *Bartonella bacilliformis* | |
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| 2 | *Klebsiella pneumoniae* | |
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| 3 | *Mycobacterium tuberculosis* | |
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| 4 | *Trypanosoma cruzi* | |
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| 5 | *Staphylococcus aureus* RF122 | |
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| 6 | *Streptococcus uberis* 0140J | |
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| 7 | *Enterococcus faecium* | |
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| 8 | *Escherichia coli* MG1655 | |
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| 9 | *Streptococcus agalactiae* NEM316 | |
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| 10 | *Pseudomonas syringae* | |
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| 11 | DENV (Dengue virus) | |
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| 12 | SARS-CoV-2 | |
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| 13 | *Homo sapiens* | |
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## Citation |
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If you use these datasets, please cite: |
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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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