Knowledge graphs in Drug Discovery

Drug research and discovery have experienced increasing attrition rates in recent years, presenting significant challenges to the pharmaceutical industry. This trend can be attributed to two primary factors. First, the era of “low-hanging fruit” in drug development has largely passed, with most straightforward, mechanistic approaches to creating blockbuster drugs having been exhausted. Consequently, researchers are now grappling with more complex biological systems underlying the remaining unmet medical needs, which require a deeper understanding of intricate cellular pathways and molecular interactions. Second, the biomedical research is facing a reproducibility crisis for a decade now, where fundamental scientific findings have become less reliable and harder to replicate, undermining the foundation upon which drug discovery efforts are built. To navigate these challenges, one need to relie on advanced knowledge management approaches, such as knowledge graphs, which can help synthesize and analyze vast amounts of scientific data to identify reliable insights and guide more effective drug discovery efforts.