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 eadk6669

Drivkrafter

Hållbar utveckling

Ämneskategorier

Farmakologi och toxikologi

Miljövetenskap

DOI

10.1126/sciadv.adk6669

PubMed

38446886

Mer information

Senast uppdaterat

2024-08-07