标题： BoKDiff：针对目标的3D分子生成的最优K扩散对齐 显示英文标题

标题： BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation

摘要： 基于结构的药物设计（SBDD）利用生物分子靶标的三维结构来指导新治疗剂的创建。 生成模型的最新进展，包括扩散模型和几何深度学习，在优化配体生成方面显示出前景。 然而，高质量蛋白质-配体复合数据的稀缺性以及生成的配体与靶标蛋白对齐的固有挑战限制了这些方法的有效性。 我们提出了BoKDiff，一种新颖的框架，通过结合多目标优化和最佳K对齐方法来增强配体生成。 建立在DecompDiff模型的基础上，BoKDiff生成多样化的候选物，并使用分子属性的加权评估（如QED、SA和对接分数）对其进行排序。 为了解决对齐问题，我们引入了一种方法，将生成的配体的质心重新定位到其对接姿态，从而实现准确的子组件提取。 此外，我们集成了最佳N（BoN）采样方法，该方法无需微调即可从多个生成的候选物中选择最优配体。 BoN取得了卓越的结果，QED值超过0.6，SA分数高于0.75，成功率达到35%以上，证明了其效率和实用性。 BoKDiff在CrossDocked2020数据集上取得了最先进的结果，包括-8.58的平均Vina对接分数和26%的分子生成成功率。 本研究首次将最佳K对齐和最佳N采样应用于SBDD，突显了它们在生成建模与实际药物发现需求之间架起桥梁的潜力。 代码可在https://github.com/khodabandeh-ali/BoKDiff.git获取。 显示英文摘要

摘要： Structure-based drug design (SBDD) leverages the 3D structure of biomolecular targets to guide the creation of new therapeutic agents. Recent advances in generative models, including diffusion models and geometric deep learning, have demonstrated promise in optimizing ligand generation. However, the scarcity of high-quality protein-ligand complex data and the inherent challenges in aligning generated ligands with target proteins limit the effectiveness of these methods. We propose BoKDiff, a novel framework that enhances ligand generation by combining multi-objective optimization and Best-of-K alignment methodologies. Built upon the DecompDiff model, BoKDiff generates diverse candidates and ranks them using a weighted evaluation of molecular properties such as QED, SA, and docking scores. To address alignment challenges, we introduce a method that relocates the center of mass of generated ligands to their docking poses, enabling accurate sub-component extraction. Additionally, we integrate a Best-of-N (BoN) sampling approach, which selects the optimal ligand from multiple generated candidates without requiring fine-tuning. BoN achieves exceptional results, with QED values exceeding 0.6, SA scores above 0.75, and a success rate surpassing 35%, demonstrating its efficiency and practicality. BoKDiff achieves state-of-the-art results on the CrossDocked2020 dataset, including a -8.58 average Vina docking score and a 26% success rate in molecule generation. This study is the first to apply Best-of-K alignment and Best-of-N sampling to SBDD, highlighting their potential to bridge generative modeling with practical drug discovery requirements. The code is provided at https://github.com/khodabandeh-ali/BoKDiff.git.