{"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\/pt\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/","title":{"rendered":"Investigadores do MIT identificam nova classe de antibi\u00f3ticos utilizando a IA"},"content":{"rendered":"

Investigadores do MIT utilizaram modelos de aprendizagem profunda para descobrir a primeira nova classe de antibi\u00f3ticos em d\u00e9cadas. Os antibi\u00f3ticos da\u00ed resultantes poderiam virar a mar\u00e9 contra as bact\u00e9rias resistentes aos medicamentos.<\/strong><\/p>\n

Quando Alexander Fleming descobriu a penicilina, o primeiro antibi\u00f3tico do mundo, estava consciente dos perigos associados.<\/p>\n

No seu discurso de aceita\u00e7\u00e3o do Pr\u00e9mio Nobel em 1945, Fleming disse: \"Depois, h\u00e1 o perigo de o homem ignorante poder facilmente subdosear-se e, ao expor os seus micr\u00f3bios a quantidades n\u00e3o letais do medicamento, torn\u00e1-los resistentes\".<\/p>\n

\u00c0 medida que foram sendo desenvolvidos novos antibi\u00f3ticos, a utiliza\u00e7\u00e3o indevida e as prescri\u00e7\u00f5es incorrectas levaram a que as bact\u00e9rias a que se destinavam se tornassem gradualmente resistentes a estes medicamentos anteriormente eficazes.<\/p>\n

A equipa que o Dr. Jim Collins dirige na O Laborat\u00f3rio Collins<\/a> no MIT iniciou o projeto Antibiotics-AI em 2020 para resolver este problema. O projeto tem um plano de sete anos para desenvolver sete novas classes de antibi\u00f3ticos para tratar sete dos agentes patog\u00e9nicos bacterianos mais mortais do mundo.<\/p>\n

Os seus esfor\u00e7os na utiliza\u00e7\u00e3o da aprendizagem autom\u00e1tica come\u00e7aram a dar frutos, uma vez que o seu trabalho<\/a> anunciou a descoberta de uma nova classe de antibi\u00f3ticos capazes de matar a bact\u00e9ria Staphylococcus aureus resistente \u00e0 meticilina (MRSA). As infec\u00e7\u00f5es por MRSA resistentes aos medicamentos matam cerca de 10.000 pessoas todos os anos s\u00f3 nos EUA.<\/p>\n

As empresas farmac\u00eauticas n\u00e3o direccionam grande parte da sua investiga\u00e7\u00e3o para os antibi\u00f3ticos porque n\u00e3o s\u00e3o t\u00e3o lucrativos como outros medicamentos. Tamb\u00e9m se tornou cada vez mais dif\u00edcil descobrir novos antibi\u00f3ticos. A IA tornou isso muito mais f\u00e1cil.<\/p>\n

Modelos de aprendizagem profunda<\/h2>\n

O desafio de criar um novo antibi\u00f3tico \u00e9 que existem quase infinitos arranjos moleculares e \u00e9 dif\u00edcil saber qual deles matar\u00e1 uma bact\u00e9ria espec\u00edfica. Para complicar a situa\u00e7\u00e3o, os investigadores t\u00eam de se certificar de que o novo composto tamb\u00e9m n\u00e3o mata as c\u00e9lulas saud\u00e1veis.<\/p>\n

A equipa do MIT criou uma base de dados com 39.000 compostos diferentes e os seus efeitos no Staphylococcus aureus. Tamb\u00e9m incorporou a citotoxicidade dos compostos, registando os seus efeitos nas c\u00e9lulas humanas do f\u00edgado, do m\u00fasculo esquel\u00e9tico e do pulm\u00e3o.<\/p>\n

Foram tamb\u00e9m adicionadas ao conjunto de dados informa\u00e7\u00f5es sobre as estruturas qu\u00edmicas dos compostos, que foram depois utilizadas para treinar um modelo de IA de aprendizagem profunda.<\/p>\n

Com base no grande conjunto de dados, o modelo foi capaz de aprender quais as estruturas qu\u00edmicas com maior probabilidade de matar as bact\u00e9rias, evitando efeitos adversos nas c\u00e9lulas saud\u00e1veis.<\/p>\n

Uma vez treinado, o modelo de IA foi utilizado para analisar 12 milh\u00f5es de compostos dispon\u00edveis no mercado. O modelo identificou mol\u00e9culas de cinco classes diferentes que, segundo as suas previs\u00f5es, matariam a bact\u00e9ria MRSA.<\/p>\n

Com base nessa lista significativamente reduzida de compostos candidatos, os investigadores obtiveram 280 compostos para testar contra a bact\u00e9ria MRSA cultivada numa placa de laborat\u00f3rio. Os seus testes levaram \u00e0 descoberta de que dois dos compostos reduziam o MRSA por um fator de 10.<\/p>\n

IA explic\u00e1vel<\/h2>\n

Os modelos de IA d\u00e3o-nos muitas vezes respostas \u00fateis, mas podem ser caixas negras inescrut\u00e1veis, n\u00e3o nos permitindo perceber como chegam \u00e0 resposta.<\/p>\n

Felix Wong, um p\u00f3s-doutorado no MIT e em Harvard e um dos principais autores do estudo, juntamente com Erica Zheng, disse: \"O que nos propusemos fazer neste estudo foi abrir a caixa negra. Estes modelos s\u00e3o constitu\u00eddos por um grande n\u00famero de c\u00e1lculos que imitam as liga\u00e7\u00f5es neuronais e ningu\u00e9m sabe realmente o que se passa por detr\u00e1s do cap\u00f4\".<\/p>\n

Compreender o que levou o modelo a selecionar os compostos que seleccionou daria aos investigadores uma melhor ideia de onde procurar medicamentos mais eficazes. A equipa de investiga\u00e7\u00e3o utilizou um algoritmo de pesquisa em \u00e1rvore de Monte Carlo adaptado para ter uma ideia do processo de tomada de decis\u00e3o do seu modelo de aprendizagem profunda.<\/p>\n

O Dr. Collins explicou: \"A ideia aqui foi que pod\u00edamos ver o que estava a ser aprendido pelos modelos para fazer as suas previs\u00f5es de que certas mol\u00e9culas seriam bons antibi\u00f3ticos\".<\/p>\n

Para al\u00e9m do MIT, a investiga\u00e7\u00e3o tamb\u00e9m contou com a contribui\u00e7\u00e3o do Broad Institute, da Integrated Biosciences, do Wyss Institute for Biologically Inspired Engineering e do Leibniz Institute of Polymer Research em Dresden, Alemanha.<\/p>\n

A utiliza\u00e7\u00e3o da IA para analisar milh\u00f5es de potenciais arranjos qu\u00edmicos est\u00e1 a ter um enorme impacto na descoberta de medicamentos<\/a>. Para o Collins Lab, \u00e9 um caso de \"um j\u00e1 foi, faltam seis\" e parece que o seu projeto de sete anos pode ter tempo de sobra.<\/p>","protected":false},"excerpt":{"rendered":"

Investigadores do MIT utilizaram modelos de aprendizagem profunda para descobrir a primeira nova classe de antibi\u00f3ticos em d\u00e9cadas. Os antibi\u00f3ticos da\u00ed resultantes poder\u00e3o vir a ser a solu\u00e7\u00e3o para as bact\u00e9rias resistentes aos medicamentos. Quando Alexander Fleming descobriu a penicilina, o primeiro antibi\u00f3tico do mundo, estava consciente dos perigos associados. No seu discurso de aceita\u00e7\u00e3o do Pr\u00e9mio Nobel em 1945, Fleming disse: \"Depois, h\u00e1 o perigo de o homem ignorante poder facilmente subdosear-se e, ao expor os seus micr\u00f3bios a quantidades n\u00e3o letais do medicamento, torn\u00e1-los resistentes.\" \u00c0 medida que foram sendo desenvolvidos novos antibi\u00f3ticos, a utiliza\u00e7\u00e3o incorrecta e as prescri\u00e7\u00f5es incorrectas levaram a que as bact\u00e9rias a que se destinavam se tornassem gradualmente resistentes a esses medicamentos.<\/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\/pt\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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. 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