A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.

Antimicrobial resistance poses a major global threat, driven by diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution, yet current empirical screening methods are expensive and often lead to suboptimal efficacy and inadvertent toxicity. We introduce CALMA, a computational framework that quantitatively analyzes the potency-toxicity landscape of multi-drug combinations. Integrating genome-scale metabolic modeling with a neural network that reflects metabolic subsystems, CALMA enhances interpretability and prioritizes pathways influencing drug interactions. The incorporation of metabolic architecture in the neural network leads to over 92% reduction in model parameters, enabling it to learn generalizable mechanistic signals and reducing the experimental search space of optimal combinations by 97%. CALMA identified promising antimicrobial combinations against