В ближайшие дни Архивач временно работает без основного хранилища ранее сохранённых картинок/видео пока мы проводим работы по техническому обслуживанию. Сохранение всего вновь поступающего контента продолжается. Но затем всё обязательно вернётся в полном объёме!
Пруфы:
https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology
https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures
https://www.nature.com/articles/d41586-020-03348-4
https://www.youtube.com/watch?v=gg7WjuFs8F4
We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks, which is a relatively modest amount of compute in the context of most large state-of-the-art models used in machine learning today.
All of the groups in this year’s competition improved, Moult says. But with AlphaFold, Lupas says, “The game has changed.” The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. “We couldn’t solve it.”
But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. “It’s almost perfect,” Lupas says. “They could not possibly have cheated on this. I don’t know how they do it.”