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Medical research is risky and expensive. Drug discovery, as an example,requires that researchers efficiently winnow thousands of potential targets toa small candidate set for more thorough evaluation. However, research groupsspend significant time and money to perform the experiments necessary todetermine this candidate set long before seeing intermediate results.Hypothesis generation systems address this challenge by mining the wealth ofpublicly available scientific information to predict plausible researchdirections. We present AGATHA, a deep-learning hypothesis generation systemthat can introduce data-driven insights earlier in the discovery process.Through a learned ranking criteria, this system quickly prioritizes plausibleterm-pairs among entity sets, allowing us to recommend new research directions.We massively validate our system with a temporal holdout wherein we predictconnections first introduced after 2015 using data published beforehand. Weadditionally explore biomedical sub-domains, and demonstrate AGATHA'spredictive capacity across the twenty most popular relationship types. Thissystem achieves best-in-class performance on an established benchmark, anddemonstrates high recommendation scores across subdomains. Reproducibility: Allcode, experimental data, and pre-trained models are available online:sybrandt.com/2020/agatha

AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach