| --- |
| 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 |
|
|
| --- |
|
|
