Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
<|reference_start|>Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design: Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful approach, excelling at unconditional sample generation and, with data-driven guidance, conditional generation within their training domain. Reliably sampling from high-value regions beyond the training data, however, remains an open challenge -- with current methods predominantly focusing on modifying the diffusion process itself. In this paper, we develop context-guided diffusion (CGD), a simple plug-and-play method that leverages unlabeled data and smoothness constraints to improve the out-of-distribution generalization of guided diffusion models. We demonstrate that this approach leads to substantial performance gains across various settings, including continuous, discrete, and graph-structured diffusion processes with applications across drug discovery, materials science, and protein design.<|reference_end|>
@article{klarner2024context-guided,
    title={Context-Guided Diffusion for Out-of-Distribution Molecular and Protein
  Design},
    author={Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane,
  Yee Whye Teh},
    journal={arXiv preprint arXiv:2407.11942},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11942},
    primaryClass={q-bio.BM cs.LG stat.ML}
}