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ZemResearch/HippoTarget

🎯 HippoTarget A curated drug-target interaction dataset, teaching LLMs which molecules bind to which proteins. 💡 Overview Welcome to HippoTarget, the fifth member of the 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… See the full description on the dataset page: https://huggingface.co/datasets/ZemResearch/HippoTarget.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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<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>

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💡 Overview

Welcome to HippoTarget, the fifth member of the 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	Molecular structures & SMILES	1.46M rows
⚗️ HippoSynth	Chemical reactions & synthesis	50K rows
🎯 HippoTarget (you are here)	Drug-target interaction	15.5K rows
🫀 HippoLv	ADMET & drug behavior in the body	~9.4K rows
☠️ 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` (uniprotbindingsingle)	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. 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.