@inproceedings{
halfon2023virtual,
title={Virtual Receptors for Efficient Molecular Diffusion},
author={Matan Halfon and Eyal Rozenberg and Ehud Rivlin and Daniel Freedman},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=EHblk6xzHe}
}
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.
Virtual Receptors for Efficient Molecular Diffusion
Matan Halfon Eyal Rozenberg Ehud Rivlin Daniel Freedman