Antimicrobial Resistance (AMR) is a big problem worldwide. Microbes like bacteria, fungi, and viruses are becoming tougher to kill with our usual medicines. Multidrug Resistance (MDR) happens when these bugs fight back against several drugs at once. The World Health Organization has highlighted the worst offenders, grouping them into Priority List-I (serious), II (high), and III (medium). A few types of bacteria are responsible for over 70% of deaths linked to AMR. Among these, Enterobacteriaceae, which includes 

Escherichia coli

, 

Salmonella enterica

, and 

Klebsiella pneumoniae

, are major culprits.

Scientists have been digging into how Machine Learning (ML) and Multitargeted Drug Design (MTDD) can help us fight AMR in these bacteria. They followed certain rules (PRISMA guidelines) and looked at lots of studies from PubMed and Scopus. The idea is that smart computers can analyze lots of data, find new ways to attack these bugs, predict how they'll resist, and make drugs that work better.

MTDD is about creating combinations of drugs that hit multiple weak spots in the bacteria. This makes it harder for them to become resistant. Using ML and MTDD together is a pretty clever way to tackle AMR in Enterobacteriaceae.

Fighting Bacterial Resistance with Smart Drugs and AI

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