09-drug-interaction-predictor

Drug Interaction Predictor

A fine-tuned Small Language Model (SLM) that predicts potential drug-drug interactions from medication lists, including severity, mechanism, evidence, and clinical recommendations.

Model Details

Intended Use

• Automated drug-drug interaction screening from medication lists
• Clinical decision support for pharmacists and prescribers
• Patient safety and pharmacovigilance workflows
• Edge deployment on Victron and other constrained hardware

Training Data

• 100,000+ synthetic drug-drug interaction examples
• Drug classes: anticoagulants, statins, antibiotics, antidepressants, PPIs, NSAIDs, opioids, immunosuppressants, and more
• Interaction types: pharmacokinetic (CYP inhibition/induction, P-gp), pharmacodynamic (additive toxicity, QT prolongation, bleeding)
• Severity levels: Critical, High, Medium, Low

Usage

model_id = "QuantumindSSI/09_drug_interaction_predictor"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Medications: Warfarin, Aspirin, Omeprazole. Analyze for interactions."""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Not a substitute for clinical pharmacologist review
• Synthetic training data may not capture all real-world interactions
• English only
• Always verify with FDA labels, clinical guidelines, and drug databases

Hardware Requirements

Citation

@misc{09_drug_interaction_predictor,
  title={Drug Interaction Predictor},
  author={QuantumIndSSI Ltd},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/QuantumindSSI/09_drug_interaction_predictor}}
}

Contact

• GitHub: https://github.com/QuantumindSSI
• HuggingFace: https://huggingface.co/QuantumindSSI