A deep representation learning algorithm on drug-target interaction to screen novel drug candidates for Alzheimer's disease.

Alzheimer's disease (AD) is a serious neurodegenerative brain disorder with complex pathophysiology. While currently available drugs can provide symptomatic benefits, they often fail to cure the disease. Thus, there is an urgent need to explore new therapeutic agents. In this study, we developed DTIP (Drug-Target Interaction Prediction), a machine learning-based approach to search novel drugs for AD by utilizing the information of drug-target interaction (DTI). By training a Skip-gram model on drug-target sequences derived from known DTI information, the algorithm learned the drug-target relationship embeddings and to predict potential drug candidates for diseases like AD. For AD, we compiled 917 risk genes and identified 292 potential drugs via the new algorithm. We further performed molecular docking by AutoDock Vina and conducted Inverted Gene Set Enrichment Analysis (IGSEA) on these drug candidates. Our results identified that several drugs could be promising for AD treatment, including human C1-esterase inhibitor, quetiapine, dasatinib, miconazole, aniracetam, chlorpromazine, hypericin, entrectinib, torcetrapib, bosutinib, sunitinib, aniracetam, rosiglitazone, tarenflurbil, milrinone, and MITO-4509. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a systematic and versatile method, our approach can also be applied to identify efficacious therapies for other complex diseases.