用修正流模型生成高亲和力药物分子，提升设计灵活性与多样性。
Rectified Flow For Structure Based Drug Design
- 基于修正流框架，可灵活加入目标优化损失和条件约束。
- 在CrossDocked2020上达-8.50平均Vina打分，75.0%分子多样性。
- 无需专门设计结合位点，适合需高效生成优质候选分子的场景。
近年来，深度生成模型在基于结构的药物设计中取得显著进展，尤其在生成能结合特定蛋白口袋的3D配体分子方面。扩散模型通过提供卓越的质量与创造性，推动了配体生成的变革。然而，传统扩散模型受限于其固有的学习目标，应用范围受限。本文提出新框架FlowSBDD，基于修正流模型，可灵活引入额外损失函数以优化特定目标，并将额外条件作为输入或替换初始高斯分布。在CrossDocked2020上的大量实验表明，该方法可在不专门设计结合位点的情况下，生成高亲和力分子，保持合理分子属性，达到最高-8.50平均Vina打分和75.0%多样性。
原文摘要 · Abstract (English)
Deep generative models have achieved tremendous success in structure-based drug design in recent years, especially for generating 3D ligand molecules that bind to specific protein pocket. Notably, diffusion models have transformed ligand generation by providing exceptional quality and creativity. However, traditional diffusion models are restricted by their conventional learning objectives, which limit their broader applicability. In this work, we propose a new framework FlowSBDD, which is based on rectified flow model, allows us to flexibly incorporate additional loss to optimize specific target and introduce additional condition either as an extra input condition or replacing the initial Gaussian distribution. Extensive experiments on CrossDocked2020 show that our approach could achieve state-of-the-art performance on generating high-affinity molecules while maintaining proper molecular properties without specifically designing binding site, with up to -8.50 Avg. Vina Dock score and 75.0% Diversity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。