Las tecnologías que Nature observará este año incluyen el diseño de proteínas utilizando algoritmos similares a los generadores de imágenes subyacentes como DALL-E, herramientas de detección de deepfake y sistemas de edición de genes que pueden modificar secuencias de ADN mucho más grandes que las posibles ediciones en un solo sitio. con CRISPR–Cas normal. Un avance que no pasó la prueba: ChatGPT. Sus aplicaciones son “ganancias que ahorran mano de obra en lugar de transformaciones del proceso de investigación”, dice el artículo.
From protein engineering and 3D printing to detection of deepfake media, here are seven areas of technology that Nature will be watching in the year ahead.
Deep learning for protein design
Two decades ago, David Baker at the University of Washington in Seattle and his colleagues achieved a landmark feat: they used computational tools to design an entirely new protein from scratch. ‘Top7’ folded as predicted, but it was inert: it performed no meaningful biological functions. Today, de novo protein design has matured into a practical tool for generating made-to-order enzymes and other proteins. “It’s hugely empowering,” says Neil King, a biochemist at the University of Washington who collaborates with Baker’s team to design protein-based vaccines and vehicles for drug delivery. “Things that were impossible a year and a half ago — now you just do it.”
Much of that progress comes down to increasingly massive data sets that link protein sequence to structure. But sophisticated methods of deep learning, a form of artificial intelligence (AI), have also been essential.
Fuente: https://www.nature.com
