Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
This work addresses a critical physical constraint in AI-driven drug design, offering a solution to improve ligand generation for therapeutic targets like COVID-19.
The paper tackled the problem of separation violations in structure-based drug design by enforcing physical distance constraints between atomic nuclei and manifolds, resulting in a 100% reduction in violation rate and up to 22.16% improvement in binding affinity compared to state-of-the-art models.
Artificial intelligence models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical constraint: atoms must maintain a minimum pairwise distance to avoid separation violation, a phenomenon governed by the balance of attractive and repulsive forces. To mitigate such separation violations, we propose NucleusDiff. It models the interactions between atomic nuclei and their surrounding electron clouds by enforcing the distance constraint between the nuclei and manifolds. We quantitatively evaluate NucleusDiff using the CrossDocked2020 dataset and a COVID-19 therapeutic target, demonstrating that NucleusDiff reduces violation rate by up to 100.00% and enhances binding affinity by up to 22.16%, surpassing state-of-the-art models for structure-based drug design. We also provide qualitative analysis through manifold sampling, visually confirming the effectiveness of NucleusDiff in reducing separation violations and improving binding affinities.