After training a neural network with data about inhibitory activities against the target bacteria, it took the AI roughly an hour and a half to narrow the list down to 240 chemicals that could help. The team started testing these chemicals in lab conditions, shortening the list down to nine. The team eventually zeroed in on an antibacterial compound called “Abaucin,” which limits crucial protein activity, and can also control how Acinetobacter baumannii worsens wounds.
Abaucin is said to be “extremely effective at killing A. baumannii but had no effect on other species of bacteria.” The latter capability — known as “narrow spectrum” in the science community — is desirable because it means the drug won’t hurt other beneficial bacteria, and won’t transfer the drug resistance to other harmful bacteria. Tests conducted on mice demonstrated that Abaucin can actually work against multiple strains of drug-resistant A. baumannii bacteria.
The team is now working on modifications so that Abaucin can be tested on human patients. Plus, work is also underway on using AI to discover chemicals that can go against other well-known drug-resistance species such as Pseudomonas aeruginosa and Staphylococcus aureus.
The World Health Organization classifies antibiotic resistance as “one of the biggest threats to global health, food security, and development today.” Worryingly, the problem is picking up pace, leading to more deaths and increased medical costs. Experts say AI can help here by reducing the cost, time, and research efforts needed for drug discovery.
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