FrameDiff: “SE(3) diffusion model with application to protein backbone generation” Yim et al. (2023)
Molecular ML Reading Group
Meeting 2: 09/27/2023
Meeting Outline
2
MMLRG Logistics
3
What is diffusion? How does it work?
4
Reverse Diffusion*
Sohl-Dickstein et al. (2015); Yang & Ermon (2019); Ho et al. (2020); Song et al. (2021)
Forward Diffusion
random prior
5
Ho et al. (2020)
Compute predicted noise
Take a step to remove the noise
(inference)
Diffusion on Protein Backbones?
Unphysical bond lengths and angles!
6
Yim et al. (2023)
Frame
Rotation
Translation
SE(3) Diffusion
7
Yim et al. (2023)
Rotation
Translation
Forward Diffusion Process
Rotation
Translation
Priors:
SE(3) Diffusion for Backbone Generation
8
(Figure 1)
Yim et al. (2023)
FrameDiff Architecture
9
Yim et al. (2023)
(Figure 2)
Sampling Algorithm of FrameDiff
10
Ho et al. (2020); Yim et al. (2023)
(Algorithm 1)
Compute predicted noise
Take a step to remove the noise
(inference)
Sample from prior
Predict true data
Estimate score
Translation denoising step
Rotation denoising step
Designability Test for Sampled Backbones
11
Yim et al. (2023)
(Figure 5)
Generation Results of FrameDiff
12
Yim et al. (2023)
(Figure 3)
Generation Results of FrameDiff
13
Yim et al. (2023)
(Figure 6)
Effect of Noise Scale on Generated Backbones
14
Yim et al. (2023)
(Figure 7)
Conclusions
15
Yim et al. (2023)
Additional Questions?
16
Topic Selection!