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Microsoft Research Podcast
Abstracts: March 21, 2024
| Chang Liu and Gretchen Huizinga
Senior Researcher Chang Liu discusses M-OFDFT, a variation of orbital-free density functional theory (OFDFT) that leverages deep learning to help identify molecular properties in a way that minimizes the tradeoff between accuracy and efficiency.
![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.](https://www.microsoft.com/en-us/research/uploads/prod/2023/06/DiG-msr-blog-hero-1400x788-1-480x280.png)
Microsoft Research Blog
Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems
| Shuxin Zheng, Chang Liu, Yu Shi, Ziheng Lu, Fusong Ju, Jianwei Zhu, Hongxia Hao, Peiran Jin, Frank Noé, Haiguang Liu, and 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.