THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction
Predicting which drug candidates will succeed using the biology known at the time
Most drugs fail in testing because their target wasn't actually causing the disease. Researchers built a knowledge graph that tracks how biomedical evidence changed over time, then used it to predict which drug candidates would advance from Phase II to Phase III trials using only the evidence available when those decisions were made. The approach outperformed direct evidence alone, especially for the 73% of candidates with no direct proof their target mattered.
Drug development costs billions and takes over a decade. If companies could identify failing programs earlier using historical evidence patterns, they could redirect resources to candidates with better odds of reaching patients. This tool lets sponsors test whether their judgment call was sound given what was actually known at the time—and might help prevent another 40–50% of Phase II failures tied to weak target-disease links.