Machine learning to design antimicrobial combination therapies: Promises and pitfalls

Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identify novel synergistic drug interactions from millions of potential combinations. Here, we compare ML-based approaches for combination therapy design based on the type of input information used, specifically: drug properties, microbial response and infection microenvironment. We also provide a compilation of publicly available drug interaction datasets relevant to AMR. Finally, we discuss limitations of current ML-based methods and propose new strategies for designing efficacious combination therapies. These include consideration of in vivo conditions, design of sequential combinations, enhancement of model interpretability and application of deep learning algorithms.

Automated summary

The text discusses the use of machine learning algorithms in designing combination therapies to combat antimicrobial resistance. It also compares different ML-based approaches based on the type of input information used and provides a compilation of relevant drug interaction datasets. Limitations of current methods are discussed, along with proposed strategies for enhancing efficacy in designing combination therapies.