Diff-Shape: A Novel Constrained Diffusion Model for Shape-based De Novo Drug Design
Keywords: Moleuclar generative model; Shape-based virtual screening; De Novo drug design;
TL;DR: A novel diffusion model, Diff-Shape, generates 3D molecular structures similar in shape to a reference, outperforming existing models by introducing a graph control plugin.
Abstract: Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an alternative way to do de novo drug design by directly generating 3D novel structures. However, existing models face challenges in reliably generating valid drug-like molecules under specific conformation constrains. Here, a novel diffusion model constrained with 3D reference shape, Diff-Shape, was proposed to generate structures whose 3D conformations are similar to a given reference shape, thereby avoiding the computational cost of screening large database of 3D conformations. This model utilized a zero-weighted graph control module, taking in various forms of point clouds of reference shape to guide diffusion process of 3D molecular generation. The results show that our model is capable of generating molecules with high shape similarity but still low 2D graph similarity to the query structure and it significantly out-performs existing shape based generative models.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 6481
Loading