  Medication recommendation is an essential task of AI for healthcare. Existing
works focused on recommending drug combinations for patients with complex
health conditions solely based on their electronic health records. Thus, they
have the following limitations: (1) some important data such as drug molecule
structures have not been utilized in the recommendation process. (2) drug-drug
interactions (DDI) are modeled implicitly, which can lead to sub-optimal
results. To address these limitations, we propose a DDI-controllable drug
recommendation model named SafeDrug to leverage drugs' molecule structures and
model DDIs explicitly. SafeDrug is equipped with a global message passing
neural network (MPNN) module and a local bipartite learning module to fully
encode the connectivity and functionality of drug molecules. SafeDrug also has
a controllable loss function to control DDI levels in the recommended drug
combinations effectively. On a benchmark dataset, our SafeDrug is relatively
shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between
recommended and actually prescribed drug combinations over previous approaches.
Moreover, SafeDrug also requires much fewer parameters than previous deep
learning-based approaches, leading to faster training by about 14% and around
2x speed-up in inference.
