Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms
Artikel i vetenskaplig tidskrift, 2024
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.
Författare
Mikael Gustavsson
Göteborgs universitet
Styrbjörn Käll
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Patrik Svedberg
Göteborgs universitet
Juan Salvador Inda Diaz
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Sverker Molander
Chalmers, Teknikens ekonomi och organisation, Environmental Systems Analysis
Jessica Coria
Göteborgs universitet
Thomas Backhaus
Göteborgs universitet
Erik Kristiansson
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Science advances
2375-2548 (eISSN)
Vol. 10 10 eadk6669Drivkrafter
Hållbar utveckling
Ämneskategorier
Farmakologi och toxikologi
Miljövetenskap
DOI
10.1126/sciadv.adk6669
PubMed
38446886