تقرير
Molecular Diffusion Models with Virtual Receptors
| العنوان: | Molecular Diffusion Models with Virtual Receptors |
|---|---|
| المؤلفون: | Halfon, Matan, Rozenberg, Eyal, Rivlin, Ehud, Freedman, Daniel |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Biology - Biomolecules |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.18330 |
| رقم الأكسشن: | edsarx.2406.18330 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |