Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

15 April 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


PFAS toxicity
Deep Transfer Learning
Uncertainty Analysis
prediction algorithm
High throughput screening


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