{"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\/es\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/","title":{"rendered":"Investigadores del MIT identifican una nueva clase de antibi\u00f3ticos mediante IA"},"content":{"rendered":"

Investigadores del MIT han utilizado modelos de aprendizaje profundo para descubrir la primera nueva clase de antibi\u00f3ticos en d\u00e9cadas. Los antibi\u00f3ticos resultantes podr\u00edan cambiar el curso de la lucha contra las bacterias resistentes a los f\u00e1rmacos.<\/strong><\/p>\n

Cuando Alexander Fleming descubri\u00f3 la penicilina, el primer antibi\u00f3tico del mundo, era consciente de los peligros que conllevaba.<\/p>\n

En su discurso de aceptaci\u00f3n del Premio Nobel en 1945, Fleming dijo: \"Luego est\u00e1 el peligro de que el hombre ignorante pueda f\u00e1cilmente subdosificarse y, al exponer sus microbios a cantidades no letales del f\u00e1rmaco, hacerlos resistentes.\"<\/p>\n

A medida que se desarrollaban nuevos antibi\u00f3ticos, el uso indebido y las prescripciones incorrectas provocaron que las bacterias a las que iban dirigidos se hicieran gradualmente resistentes a estos medicamentos antes eficaces.<\/p>\n

El equipo que dirige el Dr. Jim Collins en El laboratorio Collins<\/a> del MIT puso en marcha en 2020 el proyecto Antibiotics-AI para abordar esta cuesti\u00f3n. El proyecto tiene un plan de siete a\u00f1os para desarrollar siete nuevas clases de antibi\u00f3ticos para tratar siete de los pat\u00f3genos bacterianos m\u00e1s mort\u00edferos del mundo.<\/p>\n

Sus esfuerzos por utilizar el aprendizaje autom\u00e1tico han empezado a dar sus frutos, ya que su papel<\/a> han anunciado el descubrimiento de una nueva clase de antibi\u00f3ticos capaces de eliminar la bacteria Staphylococcus aureus resistente a la meticilina (SARM). Las infecciones por SARM resistentes a los f\u00e1rmacos causan la muerte de hasta 10.000 personas al a\u00f1o s\u00f3lo en Estados Unidos.<\/p>\n

Las empresas farmac\u00e9uticas no dedican gran parte de su investigaci\u00f3n a los antibi\u00f3ticos porque no es tan lucrativa como otros medicamentos. Adem\u00e1s, cada vez es m\u00e1s dif\u00edcil descubrir nuevos antibi\u00f3ticos. La IA lo ha hecho mucho m\u00e1s f\u00e1cil.<\/p>\n

Modelos de aprendizaje profundo<\/h2>\n

El reto de crear un nuevo antibi\u00f3tico es que existen casi infinitas combinaciones moleculares y es dif\u00edcil saber cu\u00e1l de ellas matar\u00e1 a una bacteria concreta. Para complicar a\u00fan m\u00e1s las cosas, los investigadores tienen que asegurarse de que el nuevo compuesto tampoco mate a las c\u00e9lulas sanas.<\/p>\n

El equipo del MIT cre\u00f3 una base de datos de 39.000 compuestos diferentes y sus efectos sobre el Staphylococcus aureus. Tambi\u00e9n incorporaron la citotoxicidad de los compuestos rastreando los efectos que tienen en c\u00e9lulas humanas de h\u00edgado, m\u00fasculo esquel\u00e9tico y pulm\u00f3n.<\/p>\n

Tambi\u00e9n se a\u00f1adi\u00f3 al conjunto de datos informaci\u00f3n sobre las estructuras qu\u00edmicas de los compuestos, que luego se utiliz\u00f3 para entrenar un modelo de IA de aprendizaje profundo.<\/p>\n

Bas\u00e1ndose en el amplio conjunto de datos, el modelo fue capaz de aprender qu\u00e9 estructuras qu\u00edmicas ten\u00edan m\u00e1s probabilidades de matar a las bacterias evitando al mismo tiempo efectos adversos en las c\u00e9lulas sanas.<\/p>\n

Una vez entrenado, el modelo de IA se utiliz\u00f3 para analizar 12 millones de compuestos disponibles en el mercado. El modelo identific\u00f3 mol\u00e9culas de cinco clases diferentes que, seg\u00fan predijo, matar\u00edan la bacteria MRSA.<\/p>\n

A partir de esa lista significativamente reducida de compuestos candidatos, los investigadores obtuvieron 280 compuestos para probarlos contra la bacteria MRSA cultivada en una placa de laboratorio. Sus pruebas permitieron descubrir que dos de los compuestos reduc\u00edan el SARM en un factor de 10.<\/p>\n

IA explicable<\/h2>\n

Los modelos de IA a menudo nos dan respuestas \u00fatiles, pero pueden ser cajas negras inescrutables que no nos permiten comprender c\u00f3mo llegan a la respuesta.<\/p>\n

Felix Wong, postdoctorando en el MIT y Harvard y uno de los autores principales del estudio junto con Erica Zheng, afirma: \"Lo que nos propusimos en este estudio fue abrir la caja negra. Estos modelos constan de un gran n\u00famero de c\u00e1lculos que imitan las conexiones neuronales, y nadie sabe realmente qu\u00e9 ocurre bajo el cap\u00f3\".<\/p>\n

Entender qu\u00e9 llev\u00f3 al modelo a seleccionar los compuestos que seleccion\u00f3 dar\u00eda a los investigadores una mejor idea de d\u00f3nde buscar f\u00e1rmacos m\u00e1s eficaces. El equipo de investigaci\u00f3n utiliz\u00f3 un algoritmo de b\u00fasqueda en \u00e1rbol Monte Carlo adaptado para comprender el proceso de toma de decisiones de su modelo de aprendizaje profundo.<\/p>\n

El Dr. Collins explic\u00f3: \"La idea aqu\u00ed era que pod\u00edamos ver lo que aprend\u00edan los modelos para hacer sus predicciones de que ciertas mol\u00e9culas ser\u00edan buenos antibi\u00f3ticos\".<\/p>\n

Adem\u00e1s del MIT, la investigaci\u00f3n cont\u00f3 con aportaciones del Broad Institute, Integrated Biosciences, el Wyss Institute for Biologically Inspired Engineering y el Leibniz Institute of Polymer Research de Dresde (Alemania).<\/p>\n

El uso de la IA para cribar millones de posibles acuerdos qu\u00edmicos est\u00e1 teniendo un enorme impacto en descubrimiento de f\u00e1rmacos<\/a>. Para el Laboratorio Collins, es un caso de \"uno menos, seis m\u00e1s\" y parece que su proyecto de siete a\u00f1os puede tener tiempo de sobra.<\/p>","protected":false},"excerpt":{"rendered":"

Investigadores del MIT han utilizado modelos de aprendizaje profundo para descubrir la primera nueva clase de antibi\u00f3ticos en d\u00e9cadas. Los antibi\u00f3ticos resultantes podr\u00edan cambiar el curso de la lucha contra las bacterias resistentes a los medicamentos. Cuando Alexander Fleming descubri\u00f3 la penicilina, el primer antibi\u00f3tico del mundo, era consciente de los peligros que conllevaba. En su discurso de aceptaci\u00f3n del Premio Nobel en 1945, Fleming dijo: \"Luego est\u00e1 el peligro de que el hombre ignorante pueda f\u00e1cilmente subdosificarse y, al exponer a sus microbios a cantidades no letales del f\u00e1rmaco, hacerlos resistentes\". A medida que se desarrollaban nuevos antibi\u00f3ticos, el uso indebido y la prescripci\u00f3n incorrecta hicieron que las bacterias a las que iban dirigidos se volvieran gradualmente resistentes a estos 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\/es\/2023\/12\/mit-researchers-identify-new-class-of-antibiotics-using-ai\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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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