THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction

TL;DR - THBKG is a temporal heterogeneous biomedical knowledge graph (110,396 entities, 11.1M edges, 19 relation types) where every edge is stamped with the year its evidence changed, enabling target–disease evidence profiles to be reconstructed as of any past date. It underpins a decision-aligned benchmark for predicting whether a Phase II program advances to Phase III, addressing the target–disease linkage gap blamed for 40–50% of Phase II efficacy failures.

• Each edge carries an evidence-change year, so a pair's evidence profile can be recovered exactly as it stood at its own clinical decision point — something existing biomedical KGs cannot do.
• Benchmark task: for a target–disease pair entering Phase II, predict advancement to Phase III using only evidence datable before that decision; graph propagation over THBKG outranks every direct-evidence reference under the same protocol, hitting relative success of 4.3–4.5 at top-10 pairs per therapeutic area.
• Gains concentrate on the 72.8% of pairs with no direct target–disease edge at decision time, where encoders still rank five- to sixfold above chance by propagating through intervening biology.
• A path-based explainer adapted to the decision-time subgraph decomposes predictions into the underlying evidence landscape; the graph is released as a continually updated substrate for retrospective validation.