SOURCE-LINKED INTELLIGENCE

Machine learning the microbiome for identification of novel antimicrobial peptides

CORDIS · observation · Publication date unknown

Machine learning the microbiome for identification of novel antimicrobial peptides Abuse and overuse of antibiotics has led to antimicrobial resistance (AMR), which is a major global health concern. In the EU, there are around 30,000 AMR-related deaths per year, and the incidence of AMR is on the rise; experts have predicted a loss of 10 million lives by 2050. One of the reasons to find alternatives to tackle this grave and complicated health challenge is that only very few antibiotics are discovered and those in the pipeline do not have the ability to circumvent AMR development in microbes. Thus, there is a need for discovery of novel antimicrobial agents that can take the place of conventional antibiotics. In comparison to conventional antibiotics, antimicrobial peptides (AMPs) especially bacteriocins, are effective in rapidly eradicating microbes, thereby reducing AMR-related issues.

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recordType

award

status

TERMINATED

region

EU

value

283438.8

unit

EUR