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@@ -10,7 +10,7 @@ A descriptor free approach to predicting fraction unbound in human plasma.
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  ### Model Description
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- Chemical specific parameters are either measured \emph{in vitro} or estimated using quantitative
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  structure–activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a
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  set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work,
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  we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers
 
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  ### Model Description
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+ Chemical specific parameters are either measured _in vitro_ or estimated using quantitative
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  structure–activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a
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  set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work,
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  we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers