Researchers at Queensland developed a machine‑learning tool that identifies signs of paper‑mill–generated manuscripts and applied it to the cancer literature, uncovering a wide spread of likely fraudulent papers. The tool screens for textual and image anomalies consistent with industrialized paper production; authors warn that findings threaten the integrity of reproducibility and meta‑analyses foundational to drug‑target validation. Paper mills mass‑produce fabricated or low‑quality studies that can misdirect preclinical research and inflate false leads. The new computational approach offers a scalable method to flag suspect publications for journal editors, institutions, and funders, enabling targeted retractions and corrections.