ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026
A Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining two clinically approved drugs has potential to improve treatment for common disease. But, with many thousands of combinations possible, clinically testing all pairs of drugs, with all common diseases, is not feasible. Here, we propose DRACO, a new machine learning method for discovering therapeutic drug combinations. Our model leverages a foundation model describing drug biology alongside a graph derived from clinical trials. We showcase DRACO's power to answer the question: given a drug and a health condition, what second drug would create an effective combination? In that task, 80% of our predictions have been previously reported. In the harder task of distinguishing the small number of reported combinations from millions of possible candidates, DRACO ranks 99.0% of held-out drug combinations at the highest 0.1%. We expect DRACO to be a useful tool for proposing new therapies across thousands of disease phenotypes and drug candidates.
Identifiers
42317818PMC13274323What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.