通过约束原子核间距提升药物分子生成质量
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
- 用原子核与电子云的物理约束建模，确保原子间距离合理
- 在新冠靶点上降低100%的原子分离违规率，提升22.16%亲和力
- 适合需要高精度分子结构生成的研究者使用
人工智能模型在基于结构的药物设计中展现出巨大潜力，可生成高结合亲和力的配体。然而，现有模型常忽视关键物理约束：原子必须保持最小间距以避免分离违反，这一现象由吸引与排斥力平衡决定。为缓解此类问题，我们提出NucleusDiff，通过在原子核与流形之间施加距离约束，模拟原子核与其周围电子云的相互作用。我们在CrossDocked2020数据集及一个新冠治疗靶点上对NucleusDiff进行定量评估，结果表明其可将分离违规率降低100.00%，结合亲和力提升22.16%，优于当前最先进的结构基药物设计模型。通过流形采样进行定性分析，可视化验证了NucleusDiff在减少分离违规和提升结合亲和力方面的有效性。
原文摘要 · Abstract (English)
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.
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