MFCM-DTI model of multimodal feature fusion: prediction of drug-target interaction

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field.