Integration of Transfer Learning and Multitask Learning To Predict the Potential of Per/Polyfluoroalkyl Substances in Activating Multiple Nuclear Receptors Associated with Hepatic Lipotoxicity

Per/polyfluoroalkyl substances (PFAS) can induce hepatic
lipotoxicity
by activating nuclear receptors (NRs). Here, we first developed machine-learning
models to predict activities of PFAS toward five NRs related to hepatic
lipotoxicity using five conventional algorithms based on three commonly
used data sets: a general chemical data set (A-data set, including
6388–10199 compounds), a broad PFAS data set based on OECD
definition (B-data set, including 369–772 compounds), and a
strictly defined PFAS data set (C-data set, including 184–198
compounds). Unexpectedly, the models trained on the broad chemical
spaces (A- and B-data sets) showed weak identification of active PFAS,
which might be due to distributional shifts. The C-data set-trained
models exhibited the best identification performance, but with weaker
discrimination than A-data set-trained models. There herein, a transfer-learning
multitask deep neural network (TL-MT-DNN) was implemented to transfer
knowledge from the A-data set to the C-data set, which greatly improved
the prediction performance with an average AUC of 0.886 and F1 of
0.665. Applying this model to 3716 PFAS from the PFASSTRUCTv5 database,
391 compounds were predicted to activate all the five NRs. The model’s
prediction reliability was validated by in vitro cell-based assays
and in vivo animal experiments. This study provides a modeling strategy
to improve PFAS activity prediction, overcoming the distributional
shift inherent in models trained on broad chemical spaces, and highlights
its potential for practical application in risk screening.