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Unified guidance for geometry-conditioned molecular generation.
In: (38th Conference on Neural Information Processing Systems, NeurIPS 2024, 9-15 December 2024, Vancouver). 2024. accepted (Advances in Neural Information Processing Systems ; 37)
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios.
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Publikationstyp
Artikel: Konferenzbeitrag
ISSN (print) / ISBN
1049-5258
Konferenztitel
38th Conference on Neural Information Processing Systems, NeurIPS 2024
Konferzenzdatum
9-15 December 2024
Konferenzort
Vancouver
Quellenangaben
Band: 37
Nichtpatentliteratur
Publikationen
Institut(e)
Institute of Computational Biology (ICB)