基于靶点设计分子，让生成结构更匹配蛋白口袋
Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
- 用可学习的原子数量预测器，自动匹配目标蛋白空腔大小
- 引入结合亲和力引导机制，生成分子平均得分达-8.31
- 适合需要高精度、高亲和力分子生成的研究者
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，是新药发现的重要方法。尽管近期生成模型展现出巨大潜力，但其仍存在概率动态不稳定以及生成分子尺寸与蛋白口袋几何不匹配的问题，导致质量不稳定和脱靶效应。本文提出PAFlow，一种新型靶点感知分子生成模型，包含先验交互引导与可学习原子数量预测器。该模型采用高效的流匹配框架建模生成过程，并为离散原子类型构建新的条件流匹配形式。通过集成蛋白-配体相互作用预测器，在生成过程中引导向更高亲和力区域；同时基于蛋白口袋信息设计原子数量预测器，使生成分子尺寸更好地匹配靶点几何结构。在CrossDocked2020基准上的大量实验表明，PAFlow在结合亲和力方面达到新最优水平（平均Vina得分最高达-8.31），同时保持了良好的分子性质。
原文摘要 · Abstract (English)
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.
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