ESM2-150M — это продвинутая языковая модель от Meta AI, совершившая прорыв в предсказании структуры белков на атомном уровне. Используя мощь глубокого обучения, этот ИИ анализирует аминокислотные последовательности, открывая новые возможности для биоинформатики и фармацевтики.
"Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins."