125584 Site

The study moves beyond traditional modeling by using "cross-modal attention." This AI technique allows the model to process different types of data—such as chemical structures and biological activity—simultaneously. By focusing on how these different "modes" relate to one another, the AI can pinpoint exactly which chemical groups contribute to high environmental and health risks.

: Quinolones (QNs) and Sulfonamides (SAs) were flagged as high-priority risks due to their notable contribution to Antimicrobial Resistance (AMR) . 125584

: The AI identified specific molecular groups—such as N-groups, COOH (carboxyl), C=O (carbonyl), OH (hydroxyl), and halogens —as the primary mediators of high life-cycle risks. 4. Implications for Global Health The study moves beyond traditional modeling by using

The research identifies specific high-priority groups of antibiotics that pose the greatest threats: 125584