Molecular Diffusion Models with Virtual Receptors
This work addresses computational inefficiencies in drug design for pharmaceutical research, representing an incremental improvement over existing diffusion-based methods.
The paper tackles the challenge of size disparity between drug molecules and target receptors in structure-based drug design by introducing Virtual Receptors to compress receptors while preserving structural information and incorporating protein language embeddings, resulting in improved performance and significantly faster computations.
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.