Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent generative models have shown great potential, they suffer from unstable probability dynamics and mismatch between generated molecule size and the protein pockets geometry, resulting in inconsistent quality and off-target effects. We propose PAFlow, a novel target-aware molecular generation model featuring prior interaction guidance and a learnable atom number predictor. PAFlow adopts the efficient flow matching framework to model the generation process and constructs a new form of conditional flow matching for discrete atom types. A protein-ligand interaction predictor is incorporated to guide the vector field toward higher-affinity regions during generation, while an atom number predictor based on protein pocket information is designed to better align generated molecule size with target geometry. Extensive experiments on the CrossDocked2020 benchmark show that PAFlow achieves a new state-of-the-art in binding affinity (up to -8.31 Avg. Vina Score), simultaneously maintains favorable molecular properties.

翻译：基于结构的药物设计（SBDD）旨在生成与靶标蛋白具有高结合亲和力的三维分子，是新药发现的关键途径。尽管近期生成模型展现出巨大潜力，但其存在概率动力学不稳定以及生成分子尺寸与蛋白质口袋几何结构不匹配的问题，导致生成质量不一致且易产生脱靶效应。本文提出PAFlow，一种新颖的目标感知分子生成模型，其特点在于引入先验相互作用引导和可学习的原子数目预测器。PAFlow采用高效的流匹配框架建模生成过程，并构建了一种针对离散原子类型的条件流匹配新形式。模型中整合了蛋白质-配体相互作用预测器，在生成过程中引导向量场朝向更高亲和力区域；同时设计了基于蛋白质口袋信息的原子数目预测器，以更好地使生成分子尺寸与靶标几何结构对齐。在CrossDocked2020基准上的大量实验表明，PAFlow在结合亲和力方面达到了新的最优水平（最高达-8.31平均Vina得分），同时保持了良好的分子特性。