用扩散模型与流生成网络融合，直接优化抗体结合能。
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion
- 将扩散过程建模为流生成网络状态，联合优化生成与结合能
- 结合能提升3.60%，几何重建误差降低20.40%（RMSD）
- 无需在线强化学习或伪标签，适合抗体设计初学者与开发者
互补决定区（CDRs）是抗体识别特定抗原的关键区域。现有计算方法多依赖重构损失，未协同优化结合能这一关键疗效指标；而传统结合能优化依赖计算成本高昂的在线强化学习，且依赖不可靠的能量估计算法。本文提出AbFlowNet，一种融合扩散模型与流生成网络（GFlowNet）的新生成框架。通过将每个扩散步骤视为GFlowNet中的状态，直接在训练中引入能量信号，统一了扩散生成与奖励优化。实验表明，相比基线扩散模型，AbFlowNet在氨基酸恢复率上提升3.06%，几何重建精度（RMSD）提高20.40%，结合能改善比例达3.60%。同时，无需伪标签测试集或昂贵的在线强化学习，Top-1总能量和结合能误差分别降低24.8%和38.1%。
原文摘要 · Abstract (English)
Complementarity Determining Regions (CDRs) are critical segments of an antibody that facilitate binding to specific antigens. Current computational methods for CDR design utilize reconstruction losses and do not jointly optimize binding energy, a crucial metric for antibody efficacy. Rather, binding energy optimization is done through computationally expensive Online Reinforcement Learning (RL) pipelines rely heavily on unreliable binding energy estimators. In this paper, we propose AbFlowNet, a novel generative framework that integrates GFlowNet with Diffusion models. By framing each diffusion step as a state in the GFlowNet framework, AbFlowNet jointly optimizes standard diffusion losses and binding energy by directly incorporating energy signals into the training process, thereby unifying diffusion and reward optimization in a single procedure. Experimental results show that AbFlowNet outperforms the base diffusion model by 3.06% in amino acid recovery, 20.40% in geometric reconstruction (RMSD), and 3.60% in binding energy improvement ratio. ABFlowNet also decreases Top-1 total energy and binding energy errors by 24.8% and 38.1% without pseudo-labeling the test dataset or using computationally expensive online RL regimes.
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。