SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
This addresses medication safety for patients with complex health conditions, offering an incremental improvement over existing AI-based recommendation systems.
The paper tackled the problem of recommending safe and effective drug combinations by proposing SafeDrug, a model that explicitly uses drug molecule structures and controls drug-drug interactions, resulting in a 19.43% reduction in DDI and a 2.88% improvement in Jaccard similarity over previous methods.
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.