分子ネットワーキング、適合予測、および内分泌かく乱物質を混合物中で発見するための指紋ベースモデルの改訂
Yvonne Kreutzer1, Ida Rahu1,2, Ulf Norinder3,4
1Department of Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106 91, Stockholm, Sweden.
Analytical and bioanalytical chemistry
|January 21, 2026
まとめ
機械学習や分子ネットワーキングなどの新しい手法を使用して、高リスクの環境汚染物質の優先順位付けを行いました。指紋ベースのモデルは最も低い偽陽性率を示し、廃水中の汚染物質の同定を改善しました。
背景:
- 環境汚染物質の非標的スクリーニング(NTS)では、ワークロードを管理するために高リスクの候補を優先順位付けする必要があります。; 既存の機械学習モデル(MS2Tox)は、SIRIUS+CSI:FingerIDなどのツールからの正確な分子式および指紋に依存しています。
結論:
- 分子指紋を備えた改訂MS2Toxモデルは、NTSにおける候補の優先順位付けにおいて優れたパフォーマンスを提供します。; MNおよびCPを含む組み合わせた優先順位付け戦略は、環境汚染物質の同定に可能性を示しています。; この研究は、廃水中の潜在的な内分泌かく乱化合物を正常に同定し、統合された計算アプローチの有用性を強調しました。
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