Virtual Receptors for Efficient Molecular Diffusion

Published: 28 Oct 2023, Last Modified: 14 Nov 2023NeurIPS2023-AI4Science PosterEveryoneRevisionsBibTeX
Keywords: molecules, drug design, diffusion, equivariance
Abstract: 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.
Submission Track: Original Research
Submission Number: 41