MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning

Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan

Abstract:

Combinatorial drug recommendation involves recommending a personalized combination of medication (drugs) to a patient over
his/herlongitudinal history, which essentially aims atsolving a combinatorial optimization problem that pursues high accuracy under
the safety constraint. Among existing learning-based approaches, the association between drug substructures (i.e., a sub-graph of the
molecule that contributes to certain chemical efect) and the target disease islargely overlooked, though the function of drugsin fact exhibitsstrong relevance with particular substructures. To address this issue, we propose a molecular substructure-aware encoding method entitled **MoleRec** that entails a hierarchical architecture aimed at modeling inter-substructure interactions and individual substructures’ impact on patient’s health condition, in order to identify those substructuresthat really contribute to healing patients. Specifically, MoleRec learns to attentively pooling over substructure representations which will be element-wisely re-scaled by the model’s inferred relevancy with a patient’s health condition to obtain a prior-knowledge-informed drug representation. We further design a weight annealing strategy for drug-drug-interaction (DDI) objective to adaptively control the balance between accuracy and safety criteria throughout training. Experiments on the MIMIC-III dataset demonstrate that our approach achieves new state-of-the-art performance w.r.t. four accuracy and safety metrics. Our source code is publicly available at: https://github.com/yangnianzu0515/MoleRec and MoleRec has been incorporated into the **PyHealth** package as a benchmark method for the combinatorial drug recommendation
task: https://github.com/sunlabuiuc/PyHealth.