DDPM + IPA for Protein Structure and Sequence Generation (wip)

Implementation of the DDPM + IPA (invariant point attention) for protein generation, as outlined in the paper Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models. They basically combined the invariant point attention module from Alphafold2 (used for coordinate refinement) with a standard DDPM, and demonstrate very cool infilling capabilities for protein structure generation.

I will also equip this with ability to condition on encoded text, identical to Imagen. Eventually, I will also try to offer a version using Insertion-deletion DDPM (but I have yet to replicate this work and open source it)

Citations

@misc{https://doi.org/10.48550/arxiv.2205.15019,
  doi     = {10.48550/ARXIV.2205.15019},
  url     = {https://arxiv.org/abs/2205.15019},
  author  = {Anand, Namrata and Achim, Tudor},
  keywords = {Quantitative Methods (q-bio.QM), Artificial Intelligence (cs.AI), FOS: Biological sciences, FOS: Biological sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
  title   = {Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models},
  publisher = {arXiv},
  year      = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}