Researchers have developed an AI tool called DECIPHAER that enhances our understanding of how tuberculosis (TB) drugs work by linking visual clues of bacterial behavior with detailed gene activity profiles. Aldridge highlights that while initial assumptions suggested a clinical drug killed TB by destroying its cell wall, it was actually found to impair the bacteria’s respiratory chain and energy production. This AI-driven approach allows for faster and cost-effective predictions of a drug’s molecular impact based solely on images, which is more efficient than traditional RNA sequencing. The technology not only aids in TB drug development but holds promise for accelerating treatments for other infectious diseases and cancers. By utilizing morphological profiling, DECIPHAER provides critical insights into drug mechanisms across various bacterial strains and growth conditions, supporting both laboratory studies and global collaborations aimed at advancing TB treatments.
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