Accelerating the pace of ecotoxicological assessment using artificial intelligence
| Autor: | Runsheng Song, Mengya Tao, Dingsheng Li, Yuwei Qin, Arturo A. Keller, Alexander Chang, Sangwon Suh |
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| Rok vydání: | 2021 |
| Předmět: |
Environmental toxicity
Databases Factual Geography Planning and Development Chemical Ecotoxicology Risk Assessment Databases Life cycle assessment Sensitivity distribution Aquatic species Artificial Intelligence Machine learning Environmental Chemistry Water Pollutants Factual Toxicity data Ecology Artificial neural network QSAR Chemical toxicity Bootstrapping General Medicine Lethal concentration 50 Environmental science Biochemical engineering Metric (unit) Water Pollutants Chemical Research Article |
| Zdroj: | Ambio Ambio, vol 51, iss 3 |
| ISSN: | 1654-7209 0044-7447 |
| DOI: | 10.1007/s13280-021-01598-8 |
| Popis: | Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R2 values of resulting ANN models range from 0.54 to 0.75 (median R2 = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals. |
| Databáze: | OpenAIRE |
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