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.

翻译：近年来，深度生成模型在基于结构的药物设计领域取得了巨大成功，特别是在生成与特定蛋白质口袋结合的3D配体分子方面。值得注意的是，扩散模型通过提供卓越的质量和创造性，彻底改变了配体生成。然而，传统扩散模型受限于其常规的学习目标，这限制了其更广泛的应用。在本工作中，我们提出了一种基于修正流模型的新框架FlowSBDD，该框架允许我们灵活地整合额外的损失函数以优化特定目标，并引入额外条件作为额外的输入条件或替代初始高斯分布。在CrossDocked2020数据集上的大量实验表明，我们的方法能够在无需专门设计结合位点的情况下，生成具有高亲和力的分子并保持适当的分子性质，达到-8.50的平均Vina对接分数和75.0%的多样性，实现了最先进的性能。