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Graphormer

微软研究院博客

微软研究院博客

AI saw unparalleled growth in 2023, reaching millions daily. This progress owes much to the extensive work of Microsoft researchers and collaborators. In this review, learn about the advances in 2023, which set the stage for further progress in 2024.

DeepSpeed4Science Initiative - graphic with 6 icons
微软研究院博客

| Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Martin Cai, 和 Yuxiong He

Editor’s note, Sept. 28, 2023 – The founding collaborators list was updated to correct omissions and the scientific foundation model graph was updated to correct information. In the next decade, deep learning may revolutionize the natural sciences, enhancing our capacity to…

A schematic diagram illustrating the goal of Distributional Graphormer (DiG). A molecular system is represented by a basic descriptor D, such as the amino acid sequence for a protein. DiG transforms D into a structural ensemble S, which consists of multiple possible conformations and their probabilities. S is expected to follow the equilibrium distribution of the molecular system. A legend shows a example of D and S for Adenylate kinase protein.
微软研究院博客

| Shuxin Zheng, Chang Liu, Yu Shi, Ziheng Lu, Fusong Ju, Jianwei Zhu, Hongxia Hao, Peiran Jin, Frank Noé, Haiguang Liu, 和 Tie-Yan Liu

Distributional Graphormer, Microsoft’s new deep learning framework for predicting the equilibrium distribution of molecular structures, can generate realistic and diverse molecular structures with high efficiency and low cost.

Christopher Bishop, Technical Fellow and Director, Microsoft Research AI4Science
微软研究院博客

| Christopher Bishop

Editor’s note, Oct. 20, 2023 – The post was updated to remove information related to the Amsterdam lab, as those details have since changed. Over the coming decade, deep learning looks set to have a transformational impact on the natural…