🚀 Excited to share that our work on BiaPy has been published today in Nature Methods! BiaPy makes deep learning accessible for bioimage analysis, from small datasets to terabyte-scale 3D images. 👇(1/7) 🔗 nature.com/articles/s41592-0… #DeepLearning #BioimageAnalysis #BiaPy

Delighted to share our new 🐣 work #ReScale4DL 🔬⚖️🖥️! Now available as a pre-print on bioRxiv biorxiv.org/content/10.1101/… 🥳 Did you know optimizing your AI-enhanced #BioImageAnalysis 🖥️ starts at the 🔬? How does #PixelSize affect #DL #segmentation 🧐? 👇Want to know more?👇 (1/6)

We're thrilled to present ESM3 in @ScienceMagazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.