Antibiotic resistance is a growing global health crisis that could cause 10 million deaths annually by 2050, according to the World Health Organisation. Scientists including James Collins, life sciences lead at the MIT Jameel Clinic, the epicentre of AI and health at the Massachusetts Institute of Technology, are now using AI and machine learning to drastically reduce the time and cost of identifying new antibiotics and thus widening the pipeline of drugs.
A recently published study by Jim and colleagues showed how AI-based algorithms can quickly screen thousands of potential compounds to find one that can effectively kill a drug-resistant strain of bacteria. For example, in May, he and researchers at MIT and McMaster University published a study on their use of an AI algorithm to identify an antibiotic that can kill a particularly resistant type of bacterium. The antibiotic, Abaucin, is named after the pathogen Acinetobacter baumannii, which can lead to serious infections, including meningitis and pneumonia, and is often found in hospital settings.
The discovery could profoundly impact how the drug-essay process is carried out in the future. In addition, United States legislators have proposed the PASTEUR Act which, if passed, would create an incentive program and government investment of USD 6 billion for the development of new antimicrobial drugs. AI may prove to be a powerful tool in helping us to solve the challenge of antibiotic resistance and other pressing global issues.