Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Model...

연구 요약

Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models.

IEEE journal of biomedical and health informatics 학술지에 발표된 이 연구는 Yuan S, Zhou Z, Jin X 외 연구팀이 수행하였습니다.

이 연구는 'Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models.'에 대한 과학적 분석을 제공합니다.

핵심 내용

Investigating potential interactions between drugs and herbal medicines helps optimize combined treatment strategies and supports personalized and precision medicine. Deep learning-based methods have been successful in predicting drug-related interactions. However, these methods face challenges such as low data quality and uneven distribution. Large language models (LLMs) effectively address these challenges through their extensive knowledge bases. Motivated by this, we integrate LLMs, one-hot encoding, and variational graph autoencoders (VGAEs) to propose a herbal medicine-drug interaction (HDI) prediction model. First, LLMs are employed to extract features from drug SMILES, generating high-quality molecular representations. Second, one-hot encoding is applied to herbal medicines with multiple natural products to construct feature vectors and improve model interpretability. Finally, VGAEs are utilized to reconstruct herbal medicine-drug graphs and predict unknown HDIs. Additionally, we differentiate between herbal medicine-drug similarity and the degree of individual drug or herbal medicine nodes to mitigate the dominance of high-degree nodes in VGAE message flow. Multiple experiments were conducted to validate the significance of the proposed model and its key components. This method shows great potential for applications in traditional Chinese medicine formulation optimization, new drug development, and precision medicine.

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의사/약사의 전문적 판단을 대체하지 않습니다 (PMID: 40193268)