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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