Researchers unveiled two AI advances: DeepTarget, a deep‑learning platform that predicts anti‑cancer mechanisms of small molecules and potential targets, and CarbaDetector, a machine‑learning model that identifies carbapenemase‑producing Enterobacterales from disk diffusion tests. Both tools aim to accelerate decision making in drug discovery and clinical microbiology. DeepTarget (Sanford Burnham Prebys et al., npj Precision Oncology) uses computational signatures to propose mechanisms of action for small molecules, potentially shortening target deconvolution timelines and aiding repurposing. CarbaDetector translates routine disk diffusion data into rapid classifiers for high‑priority resistance mechanisms, addressing urgent needs in antimicrobial stewardship and infection control. Both teams reported robust validation results; DeepTarget offers a route to prioritize experimental follow‑up, while CarbaDetector promises fast, low‑cost screening compatible with existing lab workflows. Adoption will depend on external validation, regulatory guidance for AI tools in diagnostics, and integration into R&D pipelines and clinical labs.