#  Geometric Deep Learning for Molecular Modeling 

 



1. Introduction to group convolutional neural networks (G-CNNs):

- Equivariance to translation
- Generalized equivariance

2. Symmetries and group representation theory 
    - Groups and homogeneous spaces
    - Group representations
    - Integrating functions on groups

3. Generalized harmonic analysis (Part I) 
    - Fourier analysis on compact groups
    - The case of rotations: SO(3)
    - Convolution theorem on compact groups
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4. Generalized Harmonic analysis (Part II) 
    - Representations of SO(3)
    - Fourier analysis on homogeneous spaces

5. Equivariance to Euclidean group SE(3) 
    - Steerable G-CNNs and tensor field networks

6. Spherical CNNs 
    - Correlation on the sphere and rotation group
    - Generalized Fast Fourier Transform

7. Simpler implementations of equivariance 
    - Equivariant graph neural network (EGNN)
    - Equivariant message passing for tensorial properties prediction

8. Equivariance and self-attention 
    - SE(3)-Transformer
    - Equivariant Transformer

9. Applications of geometric deep learning for molecular systems 
    - Quantum chemistry, molecular dynamics and drug discovery

10. Geometrical modules in AlphaFold2 
    - Pair representations and triangular updates
    - Invariant point attention (IPA)
    - Equivariant module

11. Possible extra-topics:

- Equivariant diffusion models and molecular generation
- General theory of equivariant CNNs on homogeneous spaces