README.md

---
language:
- en
license: apache-2.0
task_categories:
- text-generation
tags:
- chemistry
- smiles
- drug-target-interaction
- protein
- binding-affinity
- pharmacology
- drug-discovery
- alpaca
pretty_name: HippoTarget 15K Drug-Target Interaction Dataset
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 1896666
    num_examples: 15520
---

<div align="center">
  <h1>🎯 HippoTarget</h1>
  <p><i>A curated drug-target interaction dataset, teaching LLMs which molecules bind to which proteins.</i></p>
</div>

<p align="center">
  <img src="https://img.shields.io/badge/Dataset_Size-15.5K_Rows-blue?style=for-the-badge" alt="Size">
  <img src="https://img.shields.io/badge/Format-Apache_Parquet-green?style=for-the-badge" alt="Format">
  <img src="https://img.shields.io/badge/Language-Global_English-purple?style=for-the-badge" alt="Language">
  <img src="https://img.shields.io/badge/License-Apache 2.0-orange?style=for-the-badge" alt="License">
</p>

<hr>

## 💡 Overview

Welcome to **HippoTarget**, the fifth member of the [ZemResearch](https://huggingface.co/ZemResearch) Hippo Ecosystem. Before a drug can do anything useful in the body, it first has to bind to the right protein — like a key fitting into a lock. HippoTarget teaches LLMs exactly that: given a small molecule, which protein does it interact with?

This dataset combines real binding interaction data with a curated list of FDA-approved drugs and their known protein targets, giving models exposure to both experimental binding relationships and clinically validated drug-target pairs.

## 🧬 Part of the Hippo Ecosystem

HippoTarget is designed to work alongside the other Hippo datasets, together forming an end-to-end drug discovery pipeline:

| Dataset | Focus | Size |
|---|---|---|
| 🧬 [HippoCrates](https://huggingface.co/datasets/ZemResearch/HippoCrates) | Molecular structures & SMILES | 1.46M rows |
| ⚗️ [HippoSynth](https://huggingface.co/datasets/ZemResearch/HippoSynth) | Chemical reactions & synthesis | 50K rows |
| 🎯 **HippoTarget** (you are here) | Drug-target interaction | 15.5K rows |
| 🫀 [HippoLv](https://huggingface.co/datasets/ZemResearch/HippoLv) | ADMET & drug behavior in the body | ~9.4K rows |
| ☠️ [HippoXic](https://huggingface.co/datasets/ZemResearch/HippoXic) | Toxicology & clinical safety | ~10.6K rows |

The pipeline flows naturally: **HippoCrates** (what the molecule looks like) → **HippoSynth** (how it's made) → **HippoTarget** (what it binds to) → **HippoLv** (how it behaves in the body) → **HippoXic** (whether it's safe).

## 🧼 Curation Process

1. **Multi-source aggregation:** We combined real experimental binding interaction data with a clinically curated list of FDA-approved drugs and their protein targets.
2. **Text parsing:** Binding interaction sentences were parsed to extract SMILES structures and protein sequences using pattern matching, handling multiple sentence format variations found in the source data.
3. **RDKit validation:** Every SMILES string was validated using RDKit to confirm it represents a chemically valid molecule.
4. **Protein sequence validation:** Every protein sequence was checked to contain only standard amino acid characters within a reasonable length range.
5. **Deduplication:** We removed 199 duplicate rows based on input/output content.
6. **Result:** 15,520 clean, validated drug-target interaction rows.

## 📦 Data Sources

HippoTarget was built by merging and curating data from the following public sources:

| Source | Type | Contribution |
|---|---|---|
| `jablonkagroup/chempile-lift` (uniprot_binding_single) | Experimental binding interactions | ~6.4K rows |
| `alimotahharynia/approved_drug_target` | FDA-approved drugs & their protein targets | ~9.4K rows |

All sources are publicly available. The approved drug target data was curated with pharmacist and physician review, sourced from DrugBank, ChEMBL, ZINC20, and UniProt.

## 📝 Data Structure

HippoTarget uses the standard **Alpaca instruction-tuning format**. Each row has three columns:

- `instruction`: What the model is being asked to do (determine binding, identify target, etc.)
- `input`: The context — compound SMILES and/or protein sequence
- `output`: The expected answer

### Example Rows

**Binding Interaction:**
```json
{
  "instruction": "Determine whether this molecule interacts with the specified protein sequence.",
  "input": "Compound SMILES: COc1ccccc1N1CCN(CCN2C=CC3(CCCC3)CC2=O)CC1\nProtein sequence: MSLEQKKGADIISKILQIQNSIGKTTSPSTLKTKLSEISRKEQENARIQSKLSDLQKKKIDIDNKLLKEKQNLIKEEILERKKL...",
  "output": "Yes, this compound binds to the given protein target. The SMILES structure COc1ccccc1N1CCN(CCN2C=CC3(CCCC3)CC2=O)CC1 has documented binding affinity with the specified peptide sequence."
}
```

**Approved Drug-Target Identification:**
```json
{
  "instruction": "Identify the protein target for the following approved drug compound.",
  "input": "Drug SMILES: CC(=O)Oc1ccccc1C(=O)O",
  "output": "This approved drug compound targets the protein with sequence: MDHQDPYSVQATAAIAAAITFLILFTIFGNALVILAV..."
}
```

## 💻 How to Use

```python
from datasets import load_dataset

dataset = load_dataset("ZemResearch/HippoTarget")

print(f"Total drug-target pairs: {len(dataset['train'])}")
print(dataset['train'][0])
```

## ⚠️ Intended Use & Limitations

HippoTarget is designed for **research and educational purposes** — fine-tuning chemistry and bioinformatics LLMs, exploring drug-target interaction prediction, and building cheminformatics tools.

A few things to keep in mind:
- Protein sequences in this dataset are truncated to 200 characters for practical instruction-tuning purposes. For full-length sequences, refer to the original UniProt source.
- Binding interactions represent documented relationships from the source data, not experimentally verified binding affinity values (Kd, IC50, Ki are not included in this version).
- This dataset should not be used as a substitute for proper computational docking or wet-lab validation in real drug discovery pipelines.
- Always validate model predictions with proper bioinformatics tools before applying them to real research.

## 🤝 Citation & Collaboration

Created with ❤️ by [ZemResearch](https://huggingface.co/ZemResearch). If you use HippoTarget in your research or projects, we'd love to hear about it! Feel free to open a discussion in the community tab.