Molecular Diffusion Models with Virtual Receptors

TL;DR

This paper introduces a diffusion-based method for structure-based drug design that uses virtual receptors and protein embeddings to improve performance and speed, addressing size disparity challenges.

Contribution

The paper proposes a novel diffusion approach incorporating virtual receptors and protein language embeddings, enhancing efficiency and accuracy in drug design.

Findings

Improved performance with virtual receptors and embeddings.

Faster computation times compared to previous methods.

Effective handling of size disparity between molecules and receptors.

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.