Moreza009/Llama-DrugReasoner

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.

Model Details

• Model Name: DrugReasoner
• Training Paradigm: Group Relative Policy Optimization (GRPO)
• Input: SMILES Structure
• Output: Drug approval prediction + Rational of approval or unapproval + Confidence score
• Training Libraries: Hugging Face’s transformers, Transformer Reinforcement Learning (TRL), and Parameter-efficient fine-tuning (PEFT)
• Model Sources: meta-llama/Llama-3.1-8B-Instruct

How to Get Started with the Model

• 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 Colab 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
)

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}, 
}