A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks

Significant emphasis has been placed on advancing therapeutic interventions and medicines to treat Alzheimer's disease, the leading cause of dementia. BACE1 inhibitors have shown considerable promise in reducing amyloid-β levels in the brain and potentially halting the progression of Alzheimer disease. However, human clinical trials are fraught with risk and exorbitant cost. In addressing these challenges, this investigation has developed a deep learning model for the prediction of interactions between BACE1 inhibitors and ligand. The model represents compounds as molecular graphs and SMILES strings, which are then processed through Graph Neural Network and Convolutional Neural Network channels, respectively. This approach allows comprehensive prediction of IC50 values and classification of compound activity with the BACE1 inhibitor. The model can be used as a convenient tool for the development of BACE1 inhibitors and also for virtual screening of molecules to identify potential inhibitors.