@inproceedings{
didi2023a,
title={A framework for conditional diffusion modelling with applications in motif scaffolding for protein design},
author={Kieran Didi and Francisco Vargas and Simon Mathis and Vincent Dutordoir and Emile Mathieu and Urszula Julia Komorowska and Pietro Lio},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=k2PA7CUUJH}
}
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. 

In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature. 

However, most of these protocols are motivated heuristically, e.g. via analogies to Langevin dynamics, and lack a unifying framework, obscuring connections between the different approaches.

In this work, we unify conditional training and conditional sampling procedures under one common framework based on the mathematically well-understood Doob's h-transform. This new perspective allows us to draw connections between existing methods and propose a new conditional training protocol. We illustrate the effectiveness of this new protocol in both, image outpainting and motif scaffolding and find that it outperforms standard methods.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Kieran Didi Francisco Vargas Simon Mathis Vincent Dutordoir Emile Mathieu Urszula Julia Komorowska Pietro Lio