  Clinical trials are crucial for drug development but are time consuming,
expensive, and often burdensome on patients. More importantly, clinical trials
face uncertain outcomes due to issues with efficacy, safety, or problems with
patient recruitment. If we were better at predicting the results of clinical
trials, we could avoid having to run trials that will inevitably fail more
resources could be devoted to trials that are likely to succeed. In this paper,
we propose Hierarchical INteraction Network (HINT) for more general, clinical
trial outcome predictions for all diseases based on a comprehensive and diverse
set of web data including molecule information of the drugs, target disease
information, trial protocol and biomedical knowledge. HINT first encode these
multi-modal data into latent embeddings, where an imputation module is designed
to handle missing data. Next, these embeddings will be fed into the knowledge
embedding module to generate knowledge embeddings that are pretrained using
external knowledge on pharmaco-kinetic properties and trial risk from the web.
Then the interaction graph module will connect all the embedding via domain
knowledge to fully capture various trial components and their complex relations
as well as their influences on trial outcomes. Finally, HINT learns a dynamic
attentive graph neural network to predict trial outcome. Comprehensive
experimental results show that HINT achieves strong predictive performance,
obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and
indication outcome prediction, respectively. It also consistently outperforms
the best baseline method by up to 12.4\% on PR-AUC.
