Structure-based drug design with equivariant diffusion models

By Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Li'o, Carla P. Gomes, Max Welling and 2 more. 2022

455 citations recorded by the source corpus.

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new drug candidates. However, most existing methods focus exclusively on bottom-up de novo design of compounds or tackle other drug development challenges with task-specific models. The latter requires curation of suitable datasets, careful engineering of the models and retraining from scratch for each task. Here we show how a single pretrained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design and partial molecular design with inpainting. We formulate SBDD as a three-dimensional conditional generation problem and present DiffSBDD, an SE(3)-equivariant diffusion model that generates novel ligands conditioned on protein pockets. Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics. This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, positive): “Other studies 33–37 have shown that diffusion models are effective in modelling molecular structures beyond protein backbones, especially in the context of molecular docking and structure-based drug design.”
A Survey on Generative Diffusion Models (2022, unknown): “Material Design DiffSBDD[245]/Luo et al.”
DiffusionDet: Diffusion Model for Object Detection (2022, background): “…models have recently demonstrated remarkable results in fields including computer vision [4,21,33,35,39,68,71,75,76,82,104,108], nature language processing [3,30,52], audio processing [41,48,50,70, 91,100,103], graph-related topics [42], interdisciplinary applications [2,40,44,78,94,99,102], etc.”
Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Luo et al. (2022) [173] and DiffSBDD [243] propose to condition on 3D protein graph to generate 3D antibody or molecule with equivariant diffusion.”
Training Diffusion Models with Reinforcement Learning (2023, unknown): “Their flexibility in representing complex, high-dimensional distributions has led to the adoption of diffusion models in applications including image and video synthesis [38, 41, 20], drug and material design [56, 55, 42], and continuous control [21, 52, 15].”