New antibiotics to combat pathogen adaptability using machine learning
Pathogens, similar to bacteria, possess the ability to adapt swiftly, making it challenging to combat them effectively with antibiotics. However, a recent breakthrough by Los Alamos National Laboratory scientists has unveiled a promising approach to address this issue – leveraging machine learning to tackle pathogens.
Published in the Communication Chemistry journal, the research focuses on identifying specific molecular properties that could lead to the discovery of new antibiotics. This advancement is crucial in the face of bacteria developing resistance to existing drugs.
One of the key scientists involved in the study, Gnana Gnanakaran, emphasized the difficulty in finding compounds that can penetrate and halt bacteria resilient to antibiotics. Through their innovative method, the team delves into the molecular intricacies of bacteria, a critical step in the development of effective drugs.
The challenge posed by bacterial defenses, particularly Gram-negative bacteria with their formidable outer layers, underscores the need for novel solutions. These bacteria are adept at expelling compounds that manage to breach their barriers, rendering antibiotics less potent.
To overcome this hurdle, the research team turned to machine learning. By creating a model capable of identifying properties within certain compounds that aid in penetration and retention within bacterial defenses, they made significant progress.
The study primarily focused on Pseudomonas aeruginosa, a common infectious bacterium. By analyzing over a thousand different compounds with machine learning, the researchers gained insights into how these compounds interacted with the bacteria’s outer layer.
The findings shed light on the properties that render a compound effective against Pseudomonas aeruginosa, paving the way for similar studies on other bacteria. This breakthrough holds enormous promise in the ongoing battle against antibiotic-resistant pathogens.
