Molecular Diffusion Models with Virtual Receptors
<|reference_start|>Molecular Diffusion Models with Virtual Receptors: 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.<|reference_end|>
@article{halfon2024molecular,
    title={Molecular Diffusion Models with Virtual Receptors},
    author={Matan Halfon, Eyal Rozenberg, Ehud Rivlin, Daniel Freedman},
    journal={https://neurips.cc/virtual/2023/77389},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18330},
    primaryClass={cs.LG q-bio.BM}
}