Summary Antibiotic resistance (AR) is a high-priority urgent threat. AR pathogens such as the ESKAPE group cause millions of infections and hundreds of thousands of deaths. While current strategies such as genetic and drug screens have helped identify genes and mutations critical for AR in specific pathogens, there is a broad lack of methods to help understand AR’s origin and continuous adaptation. AR can arise in a pathogen via a variety of molecular changes, including acquiring protein domains, individual genes, or metabolic capabilities. Hence, predicting and overcoming AR in emerging pathogens or discovering new AR mechanisms requires a holistic understanding of AR evolution across multiple molecular scales. However, leveraging these diverse datasets is challenging because original databases are siloed from each other. Further, the different data types are hard to integrate in a biologically-meaningful way across scales. In this project, we describe a computational discovery framework combining evolutionary analyses and machine learning to integrate AR data across multiple scales to gain mechanistic insights into AR molecular features in ESKAPE pathogens and predict AR in new (re)emerging genomes. We will implement our approach as open FAIR data repositories, open software, and web platforms for the computational, experimental, and clinical AR communities. We will work closely with AR collaborators, end-users, and the open software community during and following the project duration to ensure the release of accessible, user-friendly, interactive platforms. Finally, in the post-award expansion phase, we will work with NIAID-funded bioinformatics consortia for downstream integration of data and methods and long-term sustainability. The framework will develop in this project will be broadly applicable to advance understanding of AR in understudied and emerging pathogens (beyond ESKAPE) towards ending the arms race between microbes and drugs by creating better treatment outcomes.