薬物相互作用のための言語モデル:現在の応用,落とし穴,そして将来の方向

Ahmad Z Al Meslamani^1,2, Abdallah Abou Hajal^1,2

¹College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates.

Expert opinion on drug metabolism & toxicology

|August 22, 2025

まとめ

大型言語モデル (LLM) は,薬物相互作用 (DDI) の抽出と予測を自動化する見通しを示しています. 臨床信頼性の説明性と信頼性などの限界に対処するためにさらなる研究が必要です.

科学分野:

薬剤監視と医薬品の安全性
医療における人工知能
バイオメディカル・インフォマティック

背景:

先進的な人工知能 (AI) フレームワーク,特に大型言語モデル (LLM) は,薬物相互作用 (DDI) のタスクのためにますます使用されています.
既知のDDIと新しいDDIを特定するためのLLMアプリケーションを詳細に説明する包括的なレビューには大きなギャップがあります.

研究の目的:

LLMベースのDDI抽出と予測の現状をレビューする.
DDIの研究にLLMを適用する際の方法論と課題を特定する.

主な方法:

主要な科学データベース (PubMed,Embase,Web of Science,Scopusなど) で広範な文献検索が行われました. 2000年1月から2025年2月まで
トランスフォーマーベースのモデル (例えば,BioBERT,GPT) を用いたDDI抽出方法と,ハイブリッドモデル,会話用エージェント,プロンプトベースの方法を用いたDDI予測方法が分析されました.

主要な成果:

LLMは,生物医学的なテキストとデータベースからDDIの抽出を自動化する可能性を示しています.
様々なLLMアーキテクチャと予測フレームワークがDDIの特定のために探索されています.

結論:

LLMは,医薬品監視と臨床意思決定支援システムの進歩に重要な可能性を秘めています.
モデルの説明性,信頼性 (幻覚),データ品質の限界に対処することは,臨床採用に不可欠です.
将来の研究は,臨床検証,説明可能なAI (XAI),データキュレーション,マルチモダルデータ統合に焦点を当てるべきである.

キーワード:

DDI抽出 DDIの予測薬物相互作用薬用警戒についてトランスフォーマーベースのフレームワークバイオメディカルテキストマイニング臨床的意思決定支援大型言語モデル

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