DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model

DrugReasoner is an AI-powered system for predicting drug approval outcomes using reasoning-augmented Large Language Models (LLMs) and molecular feature analysis. By combining advanced machine learning with interpretable reasoning, DrugReasoner provides transparent predictions that can accelerate pharmaceutical research and development.

✨ Key Features

• 🤖 LLM-Powered Predictions: Utilizes fine-tuned Llama model for drug approval prediction
• 🧬 Molecular Analysis: Advanced SMILES-based molecular structure analysis
• 🔍 Interpretable Results: Clear reasoning behind predictions for better decision-making
• 📊 Similarity Analysis: Identifies similar approved/non-approved compounds for context
• ⚡ Flexible Inference: Support for both single molecule and batch predictions

🛠️ Installation

• To use DrugReasoner, you must first request access to the base model Llama-3.1-8B-Instruct on Hugging Face by providing your contact information. Once access is granted, you can run DrugReasoner either through the command-line interface (CLI) or integrate it directly into your Python workflows.

Prerequisites

• Python 3.8 or higher
• CUDA-compatible GPU (recommended for training and inference)
• Git

Setup Instructions

1. Clone the repository

   git clone https://github.com/mohammad-gh009/DrugReasoner.git
   cd DrugReasoner

2. Create and activate virtual environment

   Windows:

   cd src
   python -m venv myenv
   myenv\Scripts\activate

   Mac/Linux:

   cd src
   python -m venv myenv
   source myenv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Login to your Huggingface account You can use this instruction on how to make an account and this on how to get the token

   huggingface-cli login --token YOUR_TOKEN_HERE

🚀 How to use

Note: GPU is required for inference. If unavailable, use our Kaggle Notebook.

CLI Inference

python inference.py \
    --smiles "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O" "CC1=CC=C(C=C1)C(=O)O" \
    --output results.csv \
    --top-k 9 \
    --top-p 0.9 \
    --max-length 4096 \
    --temperature 1.0

Python API Usage

from inference import DrugReasoner

predictor = DrugReasoner()

results = predictor.predict_molecules(
    smiles_list=["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"],
    save_path="results.csv",
    print_results=True,
    top_k=9,
    top_p=0.9,
    max_length=4096,
    temperature=1.0
)

📊 Dataset & Model

• Dataset:
• Model:

📈 Performance

DrugReasoner demonstrates superior performance compared to traditional baseline models across multiple evaluation metrics. Detailed performance comparisons are available in our paper.

📝 Citation

If you use DrugReasoner in your research, please cite our work:

@misc{ghaffarzadehesfahani2025drugreasonerinterpretabledrugapproval,
      title={DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model}, 
      author={Mohammadreza Ghaffarzadeh-Esfahani and Ali Motahharynia* and Nahid Yousefian and Navid Mazrouei and Jafar Ghaisari and Yousof Gheisari},
      year={2025},
      eprint={2508.18579},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.18579}, 
}

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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Accelerating drug discovery through AI-powered predictions