インビボ内分泌干渉物質の予測のためのマルチモダルクロスアテンティブグラフベースのフレームワーク
Eder Soares de Almeida Santos1, Gustavo Felizardo Santos Sandes1, Artur Christian Garcia da Silva2
1Laboratory of Cheminformatics, Faculty of Pharmacy, Universidade Federal de Goiás, Goiás, Brazil.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 15, 2026
まとめ
この研究は,内分泌の危険性評価のための新しいAIフレームワークを導入し,分子データと生物学的経路情報を使用して,有機体レベルの結果を正確に予測します. このモデルは,規制毒理学の透明で,機械的に解釈可能な結果を提供します.
科学分野:
- コンピューティング毒理学と化学情報学
- 内分泌障害と危険性評価について
- 逆の結果経路 (AOP) フレームワーク
背景:
- 正確で機械的に透明なモデルは,内分泌の危険性評価に不可欠です.
- 既存の方法は,しばしば透明性が欠けているか,または広範な in vivo 試験を必要とします.
- 分子データを経路情報と統合することで,予測精度を向上させることができます.
研究 の 目的:
- 生物レベルでの内分泌機能障害の結果を予測するためのマルチモダルのクロスアテンショングラフフレームワークを開発する.
- 分子グラフと有害結果経路 (AOP) に固定された測定信号を融合させる.
- 内分泌の危険性評価におけるメカニズム的透明性を高めること.
主な方法:
- Tier-1のマルチタスクグラフニューラルネットワーク (GNN) を利用し,46のin vitro ToxCast/Tox21アッセイからの主要なイベントを学習しました.
- 経路信号を分子グラフと統合するためにTier-2でクロスアテンティブマルチモダルGNNを使用しました.
- 解釈可能性のための二方向のクロスアテンションと反事実的混乱を適用した.
主要な成果:
- in vivo Hershberger (AUROC = 0.97 ± 0.014) と uterotrophic (AUROC = 0.97 ± 0.008) アッセイで高い予測性能を達成しました.
- 試験化合物の文献データと88%の一致を示した.
- 予測に影響を及ぼす重要な分子サブストラクチャとアッセイを特定しました.
結論:
- 開発されたフレームワークは,メカニズム的解釈性を持つ内分泌障害の結果を正確に予測します.
- このアプローチは,化学品の安全性評価の統合的アプローチの範囲内の標的型試験戦略をサポートします.
- このモデルは,内分泌の危険性評価の透明性と正確性を高め,規制のニーズと整合します.
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