تقرير
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
| العنوان: | Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design |
|---|---|
| المؤلفون: | Klarner, Leo, Rudner, Tim G. J., Morris, Garrett M., Deane, Charlotte M., Teh, Yee Whye |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology Statistics |
| مصطلحات موضوعية: | Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | 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. Comment: Published in the Proceedings of the 41st International Conference on Machine Learning (ICML 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.11942 |
| رقم الأكسشن: | edsarx.2407.11942 |
| قاعدة البيانات: | arXiv |
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