In a world first, scientists have discovered a new type of antibiotic using artificial intelligence (AI).
\nIt was announced by experts as a significant breakthrough in the fight against the growing problem of drug resistance. Using a powerful algorithm, more than one hundred million chemical compounds could be analyzed within days. The newly discovered compound was able to kill 35 types of potentially deadly bacteria, according to the researchers. Antibiotic-resistant infections have increased in recent years \u2013 in England, by 9% between 2017 and 2018, to almost 61,000. If antibiotics are taken incorrectly, harmful bacteria living in the body can become resistant to them, meaning the drugs may not work when truly needed. Applications of artificial intelligence, Parkinson’s, and the NHS wants to establish a national artificial intelligence laboratory. The World Health Organization (WHO) has called the phenomenon “one of the biggest threats to global health security and development today.” <\/p>\n
A New Era
\n“As far as antibiotic discovery is concerned, this is absolutely a first,” said Regina Barzilay, a lead researcher on the project at the Massachusetts Institute of Technology (MIT). The discovery was made using an algorithm inspired by the architecture of the human brain. Scientists trained it to analyze the structure of 2,500 drugs and other compounds to find those with antibacterial properties that could kill E. coli. <\/p>\n
They then selected approximately 100 candidates for physical testing before discovering Halicin. “I believe this is one of the stronger antibiotics discovered to date,” said James Collins, a bioengineer on the MIT team. “We wanted to develop a platform that allows us to harness the power of artificial intelligence to usher in a new era of antibiotic discovery.” Dr. Peter Bannister, chairman of the Healthcare panel at the Institution of Engineering and Technology, stated that the method applied is already “well-established” in medical research. “The same approach has gained popularity in the development of new therapeutics, such as pharmaceuticals and, in the case of this research, antibiotics, where pattern recognition, including deep learning, can help sort through the vast number of molecules,” he told the BBC. “This paper goes beyond theoretical simulation and presents preclinical results, which, along with subsequent clinical trials, are essential to unequivocally demonstrate the efficacy and safety of these new ‘AI-discovered’ drugs.” Since 2014, Great Britain has reduced the number of antibiotics used by more than 7%, but the number of drug-resistant bloodstream infections increased by 35% from 2013 to 2017. The researchers added that using the machine to accelerate drug discovery could help reduce the costs of producing further antibiotics in the future. <\/p>\n
Key Breakthroughs
\nThis comes just weeks after an AI-discovered drug molecule was set to be the first of its kind used in human studies. It will be deployed for the treatment of patients with obsessive-compulsive disorders (OCD). The use of AI technology in healthcare is still in its infancy, but significant breakthroughs are constantly being achieved. A recent study claimed that AI is more precise than doctors in diagnosing breast cancer based on mammograms. An international team, including researchers from Google Health and Imperial College London, designed and trained a computer model using X-rays from nearly 29,000 women. The algorithm outperformed six radiologists in reading mammograms. <\/p>\n
Machine translation of the source:<\/p>\n
https:\/\/www.bbc.com\/news\/health-51586010<\/p>\n","protected":false},"excerpt":{"rendered":"
In a world first, scientists have discovered a new type of antibiotic using artificial intelligence (AI). It was announced by experts as a significant breakthrough in the fight against the growing problem of drug resistance. Using a powerful algorithm, more than one hundred million chemical compounds could be analyzed within days. The newly discovered compound […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[253],"tags":[],"class_list":["post-8559","post","type-post","status-publish","format-standard","hentry","category-bacteriophages"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/posts\/8559","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/comments?post=8559"}],"version-history":[{"count":0,"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/posts\/8559\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/media?parent=8559"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/categories?post=8559"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.phage.help\/en\/wp-json\/wp\/v2\/tags?post=8559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}