ABSTRACT
Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2’s transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli . We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.
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