BRDKRM: An Explainable Framework for Disease Modifying Drug Identification

Abstract

Drug classification into disease-modifying (DM) and symptomatic (SYM) categories is crucial for clinical decision-making and therapeutic strategy development. To address the limitations of current methods, which often lack transparency and interpretability, we propose the Boundary Restricted Dynamic Key Route Mapping (BRDKRM) framework. This novel approach leverages the contextual overlap between disease and drug nodes in a heterogeneous graph, aggregating genes from the top K shortest paths to delineate disease neighborhood boundaries. Inspired by the classic Hansel and Gretel folklore, BRDKRM metaphorically marks boundary nodes along metapaths from disease to drug, akin to Hansel s breadcrumbs, which are then used to classify the therapeutic effect of candidate drugs. Our method achieved 86.78% accuracy in categorizing drug-disease treatments and identified 530 genes involved in both disease modification and symptomatic relief. The efficacy of BRDKRM is demonstrated through case studies on multiple sclerosis, offering an explainable approach to drug classification that bypasses extensive clinical trials. By providing biologically sound interpretations of drug classifications, our framework enhances understanding of therapeutic interventions, paving the way for more precise and efficient healthcare solutions while offering a novel approach to mapping disease-drug interactions.