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Uncertainty-Informed Deep Transfer Learning for PFAS Toxicity_CS.pdf (1.08 MB)

Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

submitted on 10.04.2021, 05:39 and posted on 15.04.2021, 06:53 by Jeremy Feinstein, ganesh sivaraman, Kurt Picel, Brian Peters, Alvaro Vazquez-Mayagoitia, Arvind Ramanathan, Margaret MacDonell, Ian Foster, Eugene Yan
In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.


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Argonne National Laboratory



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Declaration of Conflict of Interest

No conflict of interest

Version Notes

Version 1 for submission