  The identification of compound-protein interactions (CPI) plays a critical
role in drug screening, drug repurposing, and combination therapy studies. The
effectiveness of CPI prediction relies heavily on the features extracted from
both compounds and target proteins. While various prediction methods employ
different feature combinations, both molecular-based and network-based models
encounter the common obstacle of incomplete feature representations. Thus, a
promising solution to this issue is to fully integrate all relevant CPI
features. This study proposed a novel model named MCPI, which is designed to
improve the prediction performance of CPI by integrating multiple sources of
information, including the PPI network, CCI network, and structural features of
CPI. The results of the study indicate that the MCPI model outperformed other
existing methods for predicting CPI on public datasets. Furthermore, the study
has practical implications for drug development, as the model was applied to
search for potential inhibitors among FDA-approved drugs in response to the
SARS-CoV-2 pandemic. The prediction results were then validated through the
literature, suggesting that the MCPI model could be a useful tool for
identifying potential drug candidates. Overall, this study has the potential to
advance our understanding of CPI and guide drug development efforts.
