{"id":8631,"date":"2023-12-25T11:55:31","date_gmt":"2023-12-25T11:55:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=8631"},"modified":"2023-12-25T11:55:31","modified_gmt":"2023-12-25T11:55:31","slug":"mit-researchers-identify-new-class-of-antibiotics-using-ai","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/","title":{"rendered":"MIT-forskere identifiserer ny antibiotikaklasse ved hjelp av kunstig intelligens"},"content":{"rendered":"

MIT-forskere brukte dypl\u00e6ringsmodeller til \u00e5 oppdage den f\u00f8rste nye klassen av antibiotika p\u00e5 flere ti\u00e5r. Antibiotikaen som ble utviklet, kan snu kampen mot resistente bakterier.<\/strong><\/p>\n

Da Alexander Fleming oppdaget penicillin, verdens f\u00f8rste antibiotikum, var han klar over farene som var forbundet med det.<\/p>\n

I Nobelpristalen i 1945 sa Fleming: \"S\u00e5 er det en fare for at den uvitende mann lett kan underdosere seg selv og gj\u00f8re mikrobene resistente ved \u00e5 utsette dem for ikke-d\u00f8delige mengder av stoffet.\"<\/p>\n

Etter hvert som nyere antibiotika ble utviklet, f\u00f8rte misbruk og feil forskrivning til at bakteriene de var rettet mot, gradvis ble resistente mot disse tidligere effektive medisinene.<\/p>\n

Teamet som Dr. Jim Collins leder ved Collins-laboratoriet<\/a> ved MIT startet Antibiotics-AI-prosjektet i 2020 for \u00e5 l\u00f8se dette problemet. Prosjektet har en sju\u00e5rsplan for \u00e5 utvikle sju nye antibiotikaklasser for \u00e5 behandle sju av verdens d\u00f8deligste bakteriepatogener.<\/p>\n

Arbeidet med maskinl\u00e6ring har begynt \u00e5 gi resultater i form av avisen deres<\/a> kunngjorde oppdagelsen av en ny klasse antibiotika som kan drepe meticillinresistente Staphylococcus aureus-bakterier (MRSA). Bare i USA d\u00f8r opptil 10 000 mennesker hvert \u00e5r av resistente MRSA-infeksjoner.<\/p>\n

Legemiddelselskapene bruker ikke mye av sin forskning p\u00e5 antibiotika fordi det ikke er like lukrativt som andre legemidler. Det har ogs\u00e5 blitt stadig vanskeligere \u00e5 finne nye antibiotika. Kunstig intelligens har nettopp gjort det mye enklere.<\/p>\n

Modeller for dyp l\u00e6ring<\/h2>\n

Utfordringen med \u00e5 lage et nytt antibiotikum er at det finnes nesten uendelig mange molekyl\u00e6re sammensetninger, og det er vanskelig \u00e5 vite hvilken av dem som vil drepe en spesifikk bakterie. I tillegg m\u00e5 forskerne s\u00f8rge for at den nye forbindelsen ikke dreper friske celler.<\/p>\n

MIT-teamet opprettet en database med 39 000 ulike forbindelser og deres effekt p\u00e5 Staphylococcus aureus. De tok ogs\u00e5 med stoffenes cytotoksisitet ved \u00e5 spore effekten de har p\u00e5 lever-, skjelettmuskel- og lungeceller fra mennesker.<\/p>\n

Datasettet ble ogs\u00e5 tilf\u00f8rt informasjon om forbindelsenes kjemiske strukturer, som deretter ble brukt til \u00e5 trene opp en AI-modell for dyp l\u00e6ring.<\/p>\n

Basert p\u00e5 det store datasettet kunne modellen l\u00e6re hvilke kjemiske strukturer som med st\u00f8rst sannsynlighet ville drepe bakteriene og samtidig unng\u00e5 skadelige effekter p\u00e5 friske celler.<\/p>\n

Etter at AI-modellen var trent opp, ble den brukt til \u00e5 screene 12 millioner kommersielt tilgjengelige forbindelser. Modellen identifiserte molekyler fra fem ulike klasser som den foruts\u00e5 ville drepe MRSA-bakterien.<\/p>\n

Basert p\u00e5 denne listen over kandidatforbindelser, fikk forskerne 280 forbindelser som de testet mot MRSA-bakterier dyrket i en labsk\u00e5l. Testene f\u00f8rte til oppdagelsen av at to av forbindelsene reduserte MRSA med en faktor p\u00e5 10.<\/p>\n

Forklarlig AI<\/h2>\n

AI-modeller gir oss ofte nyttige svar, men de kan v\u00e6re uutgrunnelige svarte bokser som ikke gir oss innsikt i hvordan de kommer frem til svaret.<\/p>\n

Felix Wong, postdoktor ved MIT og Harvard og en av studiens hovedforfattere sammen med Erica Zheng, sier: \"Det vi fors\u00f8kte \u00e5 gj\u00f8re i denne studien, var \u00e5 \u00e5pne den svarte boksen. Disse modellene best\u00e5r av et veldig stort antall beregninger som etterligner nevrale forbindelser, og ingen vet egentlig hva som foreg\u00e5r under panseret.\"<\/p>\n

Ved \u00e5 forst\u00e5 hva som fikk modellen til \u00e5 velge de stoffene den gjorde, kan forskerne f\u00e5 en bedre id\u00e9 om hvor de skal lete etter mer effektive legemidler. Forskerteamet brukte en tilpasset Monte Carlo-trealgoritme for \u00e5 f\u00e5 et innblikk i beslutningsprosessen til den dypl\u00e6rende modellen.<\/p>\n

Dr. Collins forklarte: \"Innsikten her var at vi kunne se hva modellene l\u00e6rte for \u00e5 kunne forutsi at visse molekyler ville v\u00e6re gode antibiotika.\"<\/p>\n

I tillegg til MIT bidro ogs\u00e5 Broad Institute, Integrated Biosciences, Wyss Institute for Biologically Inspired Engineering og Leibniz Institute of Polymer Research i Dresden, Tyskland, til forskningen.<\/p>\n

Bruken av kunstig intelligens til \u00e5 sile gjennom millioner av potensielle kjemiske arrangementer har en enorm innvirkning p\u00e5 legemiddeloppdagelse<\/a>. For Collins Lab er det \"en ferdig, seks igjen\", og det ser ut til at det sju \u00e5r lange prosjektet deres kan ha tid til overs.<\/p>","protected":false},"excerpt":{"rendered":"

MIT-forskere brukte dypl\u00e6ringsmodeller til \u00e5 oppdage den f\u00f8rste nye klassen av antibiotika p\u00e5 flere ti\u00e5r. De nye antibiotikaene kan komme til \u00e5 snu kampen mot resistente bakterier. Da Alexander Fleming oppdaget penicillin, verdens f\u00f8rste antibiotikum, var han klar over farene som var forbundet med det. I sin tale da han mottok Nobelprisen i 1945, sa Fleming: \"S\u00e5 er det faren for at den uvitende mann lett kan underdosere seg selv og gj\u00f8re mikrobene resistente ved \u00e5 utsette dem for ikke-d\u00f8delige mengder av stoffet.\" Etter hvert som nyere antibiotika ble utviklet, f\u00f8rte misbruk og feil forskrivning til at bakteriene de var rettet mot, gradvis ble resistente mot disse tidligere<\/p>","protected":false},"author":6,"featured_media":8632,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[150,203,105],"class_list":["post-8631","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-benefits","tag-biotech","tag-machine-learning"],"yoast_head":"\nMIT researchers identify new class of antibiotics using AI | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MIT researchers identify new class of antibiotics using AI | DailyAI\" \/>\n<meta property=\"og:description\" content=\"MIT researchers used deep learning models to discover the first new class of antibiotics in decades. The resulting antibiotics could turn the tide against drug-resistant bacteria. When Alexander Fleming discovered penicillin, the world\u2019s first antibiotic, he was aware of the associated dangers. In his Nobel Prize acceptance speech in 1945, Fleming said, \u201cThen there is the danger that the ignorant man may easily underdose himself and by exposing his microbes to non-lethal quantities of the drug, make them resistant.\u201d As newer antibiotics were developed, misuse and incorrect prescriptions led to the bacteria they targeted gradually becoming resistant to these previously\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/nb\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-25T11:55:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/12\/Antimicrobial-susceptibility-resistance-test.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"526\" \/>\n\t<meta property=\"og:image:type\" 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