StackNAFLD: An Accurate Stacking Ensemble Learning Targeting NAFLD Treatment

Nonalcoholic fatty liver disease (NAFLD) is a slow-progressing
yet complex disease with multiple pathophysiological mechanisms that
make it challenging to treat. In this study, we developed a machine
learning (ML)-based stacking ensemble model to predict molecules that
could inhibit NAFLD progression utilizing data from animal experiments.
We systematically collected 75 agents from preclinical experiments
and classified them as inducers and inhibitors based on each study
end point. Then, we computed 12 sets of molecular fingerprints and
trained them with three baseline ML models. After that, the stacked
model was trained using the predictive features from the baseline
models and validated with 5-fold cross-validation (5-CV) and leave-one-out
cross-validation (LOOCV). We found that the stacked model outperformed
its baseline model across various evaluation metrics, thereby improving
the prediction of the NAFLD inhibitory activity. Additionally, we
tested the robustness and applicability domain of the stacked model,
ensuring that this model delivered a trustworthy prediction. Moreover,
we highlighted key molecular features, such as carboxylic, alkene,
or aromatic rings, underscoring their influence on the decision-making
of the stacked model. In conclusion, we have provided an effective
method for improving molecular property prediction by using the stacking
ensemble learning approach. Furthermore, we hosted our software in
an open-access GitHub repository for further reproducibility and use
in the drug discovery pipeline.