Scientists are exploring artificial intelligence to develop antibiotics with fewer side effects
A new study explores the potential of artificial intelligence (AI) in the development of improvements antibioticswith accurate and efficient prediction models. The use of AI has grown significantly, enabling applications such as content development, email proofreading, and self-driving cars. However, in the new study, the scientists deployed explainable AI (XAI), a branch of AI that provides justification for model judgments, which is increasingly being used by academics to examine predictive AI models.
The findings will be presented at the American Chemical Society (ACS) Fall 2024 Meeting, which will be held from August 18-22. The study was conducted by researchers at the University of Manitoba, Canada.
While XAI can be applied in a variety of contexts, the team used it to develop antibiotics. Despite AI’s near-omnipotent use, many of its models act as “black boxes,” making the decision-making process opaque. This can lead to mistrust, especially in vital domains like drug discovery.
To overcome this problem, the team used XAI to train AI drug discovery models, particularly those that identify potential new antibiotic candidates. Predictive models are essential given the pressing need for efficient antibiotics in the face of increasing resistance.
Hunter Sturm, a graduate student at the University, said: “AI is the way of the future in chemistry and drug discovery. You need someone to lay the groundwork, and I think I’m doing that.” To anticipate biological effects, the scientists fed databases of drug chemicals into an AI model. An XAI model was then used to examine the precise molecular properties behind these predictions.
Interestingly, XAI uncovered elements that human chemists would have missed, such as the fact that the non-core structures of penicillin compounds are more important than the core itself. To test predicted antibiotic compounds, researchers collaborate with microbiology labs to improve AI models by applying XAI’s insights. Rebecca Davis, a chemistry professor at the University of Manitoba in Canada, noted that “AI is very mistrustful.
“However, there is a better chance of this technology being accepted if we can ask AI to explain itself,” he added.
(With inputs from IANS)
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