Redefining the Game: MVAE-DFDPnet's Low-Dimensional Embeddings for Superior Drug-Protein Interaction Predictions

Precisely predicting drug-protein interactions (DPIs) is pivotal for drug discovery and advancing precision medicine. A significant challenge in this domain is the high-dimensional and heterogeneous data characterizing drug and protein attributes, along with their intricate interactions. In our study, we introduce a novel deep learning architecture: the
M
ulti-view
V
ariational
A
uto-
E
ncoder followed by a cascade
D
eep
F
orest (MVAE-DFDPnet). This framework adeptly learns ultra-low-dimensional embedding for drugs and proteins. Notably, our t-SNE analysis reveals that two-dimensional embedding can clearly define clusters corresponding to diverse drug classes and protein families. These ultra-low-dimensional embedding likely contribute to the enhanced robustness and generalizability of our MVAE-DFDPnet. Our model surpasses current leading methods on benchmark datasets, functioning in significantly reduced dimensional spaces. The model's resilience is further evidenced by its sustained accuracy in predicting interactions involving novel drugs, proteins, and drug classes. Additionally, we have corroborated several newly identified DPIs with experimental evidence from the scientific literature.