Screening of PFAS binding potential to PPARα using an explainable machine learning approach (IMAGE)
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We developed an explainable machine learning approach to predict the binding affinity of PFAS-PPARα with good predictive performance using only three descriptors
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Graduate School of Agriculture, Ehime University
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