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Medication recommendation is an essential task of AI for healthcare. Existingworks focused on recommending drug combinations for patients with complexhealth conditions solely based on their electronic health records. Thus, theyhave the following limitations: (1) some important data such as drug moleculestructures have not been utilized in the recommendation process. (2) drug-druginteractions (DDI) are modeled implicitly, which can lead to sub-optimalresults. To address these limitations, we propose a DDI-controllable drugrecommendation model named SafeDrug to leverage drugs' molecule structures andmodel DDIs explicitly. SafeDrug is equipped with a global message passingneural network (MPNN) module and a local bipartite learning module to fullyencode the connectivity and functionality of drug molecules. SafeDrug also hasa controllable loss function to control DDI levels in the recommended drugcombinations effectively. On a benchmark dataset, our SafeDrug is relativelyshown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity betweenrecommended and actually prescribed drug combinations over previous approaches.Moreover, SafeDrug also requires much fewer parameters than previous deeplearning-based approaches, leading to faster training by about 14% and around2x speed-up in inference.

SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations