Molecular Diffusion Models with Virtual Receptors
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911936265846784 |
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| author | Halfon, Matan Rozenberg, Eyal Rivlin, Ehud Freedman, Daniel |
| author_facet | Halfon, Matan Rozenberg, Eyal Rivlin, Ehud Freedman, Daniel |
| contents | Machine learning approaches to Structure-Based Drug Design (SBDD) have proven quite fertile over the last few years. In particular, diffusion-based approaches to SBDD have shown great promise. We present a technique which expands on this diffusion approach in two crucial ways. First, we address the size disparity between the drug molecule and the target/receptor, which makes learning more challenging and inference slower. We do so through the notion of a Virtual Receptor, which is a compressed version of the receptor; it is learned so as to preserve key aspects of the structural information of the original receptor, while respecting the relevant group equivariance. Second, we incorporate a protein language embedding used originally in the context of protein folding. We experimentally demonstrate the contributions of both the virtual receptors and the protein embeddings: in practice, they lead to both better performance, as well as significantly faster computations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Molecular Diffusion Models with Virtual Receptors Halfon, Matan Rozenberg, Eyal Rivlin, Ehud Freedman, Daniel Machine Learning Biomolecules Machine learning approaches to Structure-Based Drug Design (SBDD) have proven quite fertile over the last few years. In particular, diffusion-based approaches to SBDD have shown great promise. We present a technique which expands on this diffusion approach in two crucial ways. First, we address the size disparity between the drug molecule and the target/receptor, which makes learning more challenging and inference slower. We do so through the notion of a Virtual Receptor, which is a compressed version of the receptor; it is learned so as to preserve key aspects of the structural information of the original receptor, while respecting the relevant group equivariance. Second, we incorporate a protein language embedding used originally in the context of protein folding. We experimentally demonstrate the contributions of both the virtual receptors and the protein embeddings: in practice, they lead to both better performance, as well as significantly faster computations. |
| title | Molecular Diffusion Models with Virtual Receptors |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2406.18330 |