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AI used to predict potential new antibiotics in groundbreaking study
Scientists used an algorithm to mine ‘the entirety of the microbial diversity’ on Earth, speeding up antibiotic resistance research
To validate those predictions, they used chemistry to synthesize 100 of those molecules in the laboratory and then test them to determine if they could actually kill bacteria, including “some of the most dangerous pathogens in our society”, de la Fuente said. Google DeepMind recently released the latest version of AlphaFold, a program that predicts how proteins will interact with other molecules and ions, which could produce breakthroughs in fields as varied as cancer therapy and crop resilience. Anthony Gitter, a University of Wisconsin-Madison associate professor of biostatistics and medical informatics who uses machine learning in biological experiments, says the “significance of the advance” in the Cell paper “was due to top-tier bioinformatics research as opposed to automated science enabled by AI”.
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