  The escalating drug addiction crisis in the United States underscores the
urgent need for innovative therapeutic strategies. This study embarked on an
innovative and rigorous strategy to unearth potential drug repurposing
candidates for opioid and cocaine addiction treatment, bridging the gap between
transcriptomic data analysis and drug discovery. We initiated our approach by
conducting differential gene expression analysis on addiction-related
transcriptomic data to identify key genes. We propose a novel topological
differentiation to identify key genes from a protein-protein interaction (PPI)
network derived from DEGs. This method utilizes persistent Laplacians to
accurately single out pivotal nodes within the network, conducting this
analysis in a multiscale manner to ensure high reliability. Through rigorous
literature validation, pathway analysis, and data-availability scrutiny, we
identified three pivotal molecular targets, mTOR, mGluR5, and NMDAR, for drug
repurposing from DrugBank. We crafted machine learning models employing two
natural language processing (NLP)-based embeddings and a traditional 2D
fingerprint, which demonstrated robust predictive ability in gauging binding
affinities of DrugBank compounds to selected targets. Furthermore, we
elucidated the interactions of promising drugs with the targets and evaluated
their drug-likeness. This study delineates a multi-faceted and comprehensive
analytical framework, amalgamating bioinformatics, topological data analysis
and machine learning, for drug repurposing in addiction treatment, setting the
stage for subsequent experimental validation. The versatility of the methods we
developed allows for applications across a range of diseases and transcriptomic
datasets.

Biomolecules;Machine Learning;Genomics