Add InteractBind benchmark dataset

.gitattributes

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README.md

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----
-license: mit
----
+---
+license: cc-by-4.0
+configs:
+- config_name: affinity
+  default: true
+  data_files:
+  - split: train
+    path: data/affinity/data.csv
+- config_name: p_ood_25
+  data_dir: data/p_ood_25
+- config_name: p_ood_28
+  data_dir: data/p_ood_28
+- config_name: p_ood_31
+  data_dir: data/p_ood_31
+- config_name: p_ood_33
+  data_dir: data/p_ood_33
+---
+
+# InteractBind  
+
+> A physically grounded, large-scale protein–ligand interaction dataset  
+> for interpretable and interaction-aware binding prediction  
+
+---
+
+## Motivation  
+
+Most existing protein–ligand binding datasets provide only coarse-grained supervision, such as binary labels or scalar affinity values. While effective for prediction, these signals compress complex molecular interaction processes into a single outcome, limiting interpretability and mechanistic understanding.
+
+**InteractBind** addresses this limitation by explicitly modelling *non-covalent interaction patterns* derived from experimentally resolved protein–ligand complexes.  
+
+It enables **token-level supervision**, bridging sequence-based representations with physically meaningful interaction structures.
+
+---
+
+## Dataset Overview  
+
+InteractBind is constructed from high-quality experimentally resolved complexes and includes:
+
+- Protein sequences (FASTA and structure-aware sequence)
+- Ligand molecular representations (SMILES and SELFIES)
+- Binding labels and affinity annotations
+- Token-level non-covalent interaction maps  
+
+The dataset is designed to support both **prediction accuracy** and **mechanistic interpretability**.
+
+---
+## Dataset
+
+This repository provides benchmark CSVs with true residue-level interaction maps for PLI prediction evaluation.
+
+| Dataset | Type | Example Use |
+|----------|------|--------------|
+| InteractBind (affinity) | Binding affinity splits | Evaluate in-domain |
+| InteractBind-P-25%/28%/31%/33% OOD | Protein OOD splits | Evaluate novel protein generalisation |
+
+## Files
+
+The Hugging Face Dataset Viewer is configured to read the CSV subsets under `data/`:
+
+- `affinity`: the full InteractBind affinity table.
+- `p_ood_25`, `p_ood_28`, `p_ood_31`, `p_ood_33`: protein OOD benchmark subsets with `train`, `validation`, and `test` splits.
+
+Each CSV includes seven residue-level binding-site fingerprint columns derived from the interaction maps:
+
+- `Hydrogen bonding_binding_site`
+- `Salt Bridges_binding_site`
+- `π–π Stacking_binding_site`
+- `Cation–π_binding_site`
+- `Hydrophobic_binding_site`
+- `Van der Waals_binding_site`
+- `Overall_binding_site`
+
+Each value is a binary list aligned to the protein FASTA sequence. For example, `[0,0,1,0]` marks the third residue as a binding-site residue. Negative protein-ligand pairs without contact-map entries are encoded as all-zero fingerprints.
+
+## Supported Interaction Types  
+
+Structured annotations are provided for major non-covalent interaction categories:
+
+- Hydrogen bonds  
+- Hydrophobic interactions  
+- Salt bridges  
+- π–π stacking  
+- π–cation interactions  
+- Van der Waals contacts  
+
+Each interaction channel can be used independently or combined for multi-channel supervision.
+
+---
+
+## Key Features  
+
+- **Physically grounded supervision**  
+  Derived from experimentally resolved complexes rather than heuristic attention signals.  
+
+- **Token-level interaction maps**  
+  Enables fine-grained modelling of residue–atom interactions.  
+
+- **Model-agnostic integration**  
+  Compatible with sequence-based encoders (e.g., ESM, SELFormer, and other protein–ligand models).  
+
+- **Interpretability support**  
+  Facilitates binding residue identification and interaction pattern analysis.  
+
+- **Scalable design**  
+  Allows large-scale training without requiring full structural modelling during inference.  
+
+---
+
+## Research Applications  
+
+InteractBind supports a broad range of research directions:
+
+- Protein–ligand binding prediction  
+- Binding site/pocket localisation 
+- Interaction-aware representation learning  
+- Mechanistic hypothesis generation  
+- Drug discovery and virtual screening  
+- Explainable AI for molecular modelling  
+
+---

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