Advancing Drug Discovery through Integrative Computational Models and AI Technologies

The intricate, costly, and time-intensive nature of traditional drug discovery processes delays the development of novel pharmaceuticals. We proposed a drug repurposing workflow by integrating computational models, artificial intelligence, and molecular biology techniques to streamline drug discovery and enhance pharmacological research. This workflow moves beyond conventional methods, focusing on protein interactions and multiscale molecular analyses. Our approach addresses critical limitations in current methodologies: the oversimplification of receptor–ligand interactions, static representation of protein structures, and neglect of the complex electronic distributions in molecular interactions. Central to our methodology is enriching a comprehensive knowledge graph, integrating data from scientific literature and multiple databases such as UniProt for genes, PDB for protein structures, and ChEMBL for molecules. This knowledge graph is further enhanced by incorporating predicted drug–target and protein–protein interaction scores derived from structural similarity analyses. To demonstrate its potential, we applied this workflow to a specific use case, uncovering new insights into the mechanisms of action for repurposed drugs. This integration provides profound insights into the mechanistic processes underlying these interactions, establishing a new framework for biomedical research. Ultimately, this research enables a more accurate modeling of biological systems’ complex nature, facilitating the discovery of more effective and tailored medical treatments.