ABSTRACT Our ability to treat infectious diseases is impeded by two major problems. One is the rapid increase of antimicrobial resistance, and the other is the prohibitive cost and time required for discovering new drugs. A potential approach to overcome these problems is to focus on repurposing existing drugs for host-directed therapy. However, this is an emerging application area. While several studies have used this broad approach to find drug candidates for specific viruses and bacterial infections, there is a dearth of systematic computational frameworks that can be used to repurpose drugs for any infectious disease, especially ones that focus on drug and disease mechanisms rather than individual drug and target properties. Also missing are frameworks that can leverage the massive amounts of data and knowledge available for non-infectious diseases to tackle infectious disease treatment. In this project, we will develop an integrative framework that uses mechanism- guided, interpretable machine learning (ML) models to repurpose drugs to bolster host response to infection. Our framework leverages massive transcriptome data collections and genome-scale human gene interaction networks; these are two complementary sources of information about molecular mechanisms relevant for this repurposing effort. It also uses data and knowledge about hundreds of non-infectious diseases and thousands of small molecules (including FDA-approved drugs) to create numerous repurposing opportunities. Requiring only host transcriptome data in response to infection, our general-purpose ML framework will be applicable to new, emerging, and understudied infectious diseases. This project will also result in high-confidence drug candidates for several infectious diseases along with mechanistic insights into new avenues for host-directed therapeutics.