Antimicrobial resistance poses a major global threat due to the diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution, yet current empirical methods often lead to suboptimal efficacy and inadvertent toxicity. The high cost of experimentally testing numerous combinations underscores the need for data-driven methods to streamline treatment design. We introduce CALMA, an approach that predicts the potency and toxicity of multi-drug combinations in Escherichia coli and Mycobacterium tuberculosis. CALMA identified synergistic antimicrobial combinations involving vancomycin and isoniazid that were antagonistic for toxicity, which were validated using in vitro cell viability assays in human cell lines and through mining of patient health records that showed reduced side effects in patients taking combinations identified by CALMA. By combining mechanistic modelling with deep learning, CALMA improves the interpretability of neural networks, identifies key pathways influencing drug interactions, and prioritizes combinations with enhanced potency and reduced toxicity.

SC and HSA are inventors on a patent application by UM related to drug combination toxicity prediction with deep learning. RV is an employee of Komodo Health. The authors declare that they have no other competing interests. 

This work was supported by faculty start-up funds from the University of Michigan (UM), R01AI150826 from National Institute of Allergy and Infectious Diseases, R35GM137795 from National Institute of General Medical Sciences, UM Endowment for Basic Sciences Accelerator Award, UM Research Scouts Award to S.C.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All data were de-identified in compliance with HIPAA de-identification standards. Komodo Health determination confirmed that no protected health information remained in the dataset. As the study was conducted on a pre-existing de-identified dataset, it was exempt from IRB review and informed consent requirements.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors. This paper analyzes data from the Komodo Health Platform (2016-2025) after signing a data-sharing agreement.