新しいAI手法で有害化学物質を検出可能(Toxic chemicals can be detected with new AI method)

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2024-05-02 チャルマース工科大学

化学物質は家庭から産業まで広範囲に使われており、多くが水路や生態系に悪影響を与えます。特にPFASが問題視されています。従来の規制では動物実験に時間と多くの動物が使われ、新しい化学物質の毒性を判別するのが困難です。スウェーデンの研究者はAIを使って化学物質の毒性を迅速かつ低コストで評価する方法を開発しました。これにより動物実験の削減や新しい化学物質の開発が期待されています。

<関連情報>

トランスフォーマーは、水生生物における化学物質の急性および慢性毒性を正確に予測することができる。 Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

MIKAEL GUSTAVSSON, STYRBJÖRN KÄLL, PATRIK SVEDBERG, JUAN S. INDA-DIAZ, […], AND ERIK KRISTIANSSON
Science Advances  Published: 6 Mar 2024
DOI:https://doi.org/10.1126/sciadv.adk6669

Abstract

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups—algae, aquatic invertebrates and fish—and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

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