The medical field has relied increasingly on multi-drug therapies to combat antibiotic resistance. Combinations are often chosen empirically leading to suboptimal treatment outcomes and spread of resistance. Recent methods to develop synergistic treatments are dependent on computational models to cut down the vast sample size of combinations available from thousands of FDA approved antimicrobials. Our model uses a unique combination of computationally calculated drug – protein interactions, multi-omics studies, and machine learning (ML) to predict effective drug combination therapies. Computational calculations performed as well as the more costly omics datasets at predicting synergistic drug combinations. In addition to predicting drug - drug interactions, our models reveal underlying biochemical pathways related to synergy and drug mechanisms of action.

This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.