Unified Guidance for Geometry-Conditioned Molecular Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916552150876160 |
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| author | Ayadi, Sirine Hetzel, Leon Sommer, Johanna Theis, Fabian Günnemann, Stephan |
| author_facet | Ayadi, Sirine Hetzel, Leon Sommer, Johanna Theis, Fabian Günnemann, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unified Guidance for Geometry-Conditioned Molecular Generation Ayadi, Sirine Hetzel, Leon Sommer, Johanna Theis, Fabian Günnemann, Stephan Biomolecules Machine Learning 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. |
| title | Unified Guidance for Geometry-Conditioned Molecular Generation |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2501.02526 |