Enhancing Precision Drug Recommendations via In-Depth Exploration of Motif Relationships

Abstract

Making accurate and safe clinical decisions for patients has long been a challenging task. With the proliferation of electronic health records and the rapid advancement of technology, drug recommender systems have emerged as invaluable aids for healthcare professionals, offering precise and secure prescriptions. Among prevailing methods, the exploration of motifs, defined as substructures with specific biological functions, has largely been overlooked. Nevertheless, the substantial impact of the motifs on drug efficacy and patient diseases implies that a more extensive incorporation could potentially improve the recommender systems. In light of this, we introduce DEPOT , an innovative drug recommendation framework developed from a motif-aware perspective. In our approach, we employ chemical decomposition to partition drug molecules into semantic motif-trees and design a structure-aware graph transformer to capture motif collaboration. This innovative practice preserves the topology knowledge and facilitates perception of drug functionality. To delve into the dynamic correlation between motifs and disease progression, we conduct a meticulous investigation from two perspectives: repetition and exploration. This comprehensive analysis allows us to gain valuable insights into the drug turnover, with the former focusing on reusability and the latter on discovering new requirements. We further formulate a historical weighting strategy for drug-drug interaction (DDI) objective, enabling adaptive control over the trade-off between accuracy and safety criteria throughout the training process. Extensive experiments conducted on four data sets validate the effectiveness and robustness of DEPOT .