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README.md

@@ -1,71 +1,63 @@
----
-license: mit
----
+# ReverseLigQ dataset (Hugging Face)
 
-# ReverseLigQ Datasets
+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).
 
-This repository contains the curated datasets used by **ReverseLigQ**, a platform designed to search for potential protein targets (molecular targets) of bioactive compounds.
+## Directory layout
 
-The data stored here includes ligand representations, mappings between internal indices and compound identifiers, and curated associations between ligands and Pfam domains derived from structural and sequence information.
-
----
-
-## Contents
-
-### 1. Embeddings and Fingerprints
-
-- **`comps_embs.npy`**  
-  NumPy array containing **ChemBERTa embeddings** for ligands derived from **PDB** and **ChEMBL**.  
-  Each row corresponds to a ligand, and the position in the array is linked to the internal index (`idx`) used throughout ReverseLigQ.
-
-- **`comps_fps.npy`**  
-  NumPy array containing **Morgan fingerprints** (circular fingerprints, ECFP-like) for the same set of ligands from **PDB** and **ChEMBL**.  
-  As with `comps_embs.npy`, rows are aligned with the internal ligand index (`idx`).
-
----
-
-### 2. Index–Identifier Mappings
+```
+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
+```
 
-- **`id_to_idx.pkl`**  
-  Python dictionary mapping **ligand identifiers** (e.g., from PDB or ChEMBL) to the **internal index** (`idx`) used in the arrays `comps_embs.npy` and `comps_fps.npy`.  
-  This mapping allows you to go from an external ligand ID to the corresponding row in the embedding and fingerprint matrices.
+## Compound data (`compound_data/pdb_chembl/`)
 
-- **`idx_to_id.pkl`**  
-  Inverse mapping of `id_to_idx.pkl`.  
-  It is a Python dictionary mapping the internal index (`idx`) back to the **original ligand identifier**.  
-  This is useful, for example, when retrieving nearest neighbors in embedding space by index and then needing to recover the original IDs.
+### `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)
 
-### 3. Ligand–Pfam Domain Associations
+### 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.)
 
-- **`ligs_fams_curated.pkl`**  
-  Python dictionary mapping ligands to **Pfam domains they are known to bind**, based on **curated, high-confidence evidence**.  
-  This curated set includes ligands whose associated domain can be determined unambiguously from:
-  - a **resolved 3D structure** (e.g., a PDB complex where the domain is clearly defined),
-  - a **protein with a single domain**, or
-  - **strong structural similarity** to another ligand whose interaction has been experimentally confirmed in a solved 3D structure.  
-  This file represents the **high-confidence (curated) ligand–domain associations** used by ReverseLigQ.
+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`.
 
-- **`ligs_fams_possible.pkl`**  
-  Python dictionary mapping ligands to **possible Pfam domains they may bind**, in cases where the specific domain cannot be determined with certainty.  
-  Here, the reference protein:
-  - often contains **multiple domains**,
-  - does **not** have a resolved 3D structure for the ligand–protein complex, and therefore  
-  - it is unclear which of the possible domains is the true binding site.  
-  This file therefore represents **putative or ambiguous ligand–domain associations**, complementing the curated set.
+## 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.
 
-### 4. Organism-Specific Datasets
+- `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)
 
-ReverseLigQ integrates multiple organisms, each identified by an integer key.  
-The files in this section provide organism-level ligand lists, protein descriptions, and Pfam-based protein families.
+### Organism keys
 
-Organism keys and corresponding species:
+ReverseLigQ integrates multiple organisms, each identified by an integer key:
 
 | Key | Organism |
-|-----|----------|
+|---:|---|
 | 1 | *Bartonella bacilliformis* |
 | 2 | *Klebsiella pneumoniae* |
 | 3 | *Mycobacterium tuberculosis* |
@@ -80,98 +72,8 @@ Organism keys and corresponding species:
 | 12 | SARS-CoV-2 |
 | 13 | *Homo sapiens* |
 
-#### Files:
-
-- **`ligand_lists.pkl`**  
-  Python dictionary containing the **list of ligands associated with each organism** included in the platform.  
-  Keys correspond to organism indices (1–13), and values are lists of ligand identifiers.
-
-- **`prot_descriptions.pkl`**  
-  Python dictionary containing **protein descriptions** (annotations, functional summaries, etc.) for each protein present in the integrated organisms.  
-
-- **`fam_prot_dict.pkl`**  
-  Python dictionary mapping each organism to its **proteins grouped by Pfam families**.  
-  For each organism key, the value is another dictionary where:
-  - keys are Pfam domain identifiers, and  
-  - values are lists of proteins belonging to that domain family.
-
-These files enable ReverseLigQ to perform organism-specific ligand searches, identify relevant targets, and reconstruct ligand–domain landscapes across diverse pathogens and host species.
-
----
-
-## Usage Notes
-
-- The **internal index** (`idx`) is the key that ties together:
-  - rows in `comps_embs.npy`
-  - rows in `comps_fps.npy`
-  - entries in `id_to_idx.pkl` / `idx_to_id.pkl`
-- The **Pfam-domain dictionaries** (`ligs_fams_curated.pkl` and `ligs_fams_possible.pkl`) can be used to:
-  - build training or evaluation sets for ligand–target or ligand–domain prediction,
-  - separate high-confidence from ambiguous associations,
-  - benchmark methods that infer protein targets from ligand similarity.
-- The **organism-level dictionaries** (`ligand_lists.pkl`, `prot_descriptions.pkl`, `fam_prot_dict.pkl`) support:
-  - organism-specific prioritization of targets,
-  - analyses of Pfam family distribution across species,
-  - integration of ligand knowledge with genomic and proteomic data.
-
----
-
-## Example 1 (Python)
-
-```python
-import numpy as np
-import pickle
-
-# Load embeddings and fingerprints
-embs = np.load("comps_embs.npy")
-fps = np.load("comps_fps.npy")
-
-# Load mappings
-with open("id_to_idx.pkl", "rb") as f:
-    id_to_idx = pickle.load(f)
-
-with open("idx_to_id.pkl", "rb") as f:
-    idx_to_id = pickle.load(f)
-
-# Load curated ligand–Pfam associations
-with open("ligs_fams_curated.pkl", "rb") as f:
-    ligs_fams_curated = pickle.load(f)
-
-# Example: get embedding for a given ligand ID
-lig_id = "CHEMBL123456"
-idx = id_to_idx[lig_id]
-lig_emb = embs[idx]
-lig_fp = fps[idx]
-```
-
-## Example 2 (Python)
-
-```python
-import pickle
-
-# Load curated and possible ligand–Pfam associations
-with open("ligs_fams_curated.pkl", "rb") as f:
-    ligs_fams_curated = pickle.load(f)
-
-with open("ligs_fams_possible.pkl", "rb") as f:
-    ligs_fams_possible = pickle.load(f)
-
-lig_id = "CHEMBL123456"
-
-# High-confidence binding domains (if available)
-curated_domains = ligs_fams_curated.get(lig_id, [])
-
-# Possible (ambiguous) domains
-possible_domains = ligs_fams_possible.get(lig_id, [])
-
-print("Curated Pfam domains:", curated_domains)
-print("Possible Pfam domains:", possible_domains)
-```
-
 ## 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
+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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