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