标题： 从B细胞进化中学习：通过在线优化进行抗体设计的自适应多专家扩散 显示英文标题

标题： Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization

摘要： 扩散模型的最新进展在抗体设计方面展现出巨大的潜力，但现有的方法采用统一的生成策略，无法适应每个抗原的独特需求。 受B细胞亲和力成熟启发，其中抗体通过多目标优化平衡亲和力、稳定性和自我避免进化，我们提出了第一个生物启发框架，该框架在一个在线元学习系统中利用基于物理的领域知识。 我们的方法使用多个专业专家（范德华力、分子识别、能量平衡和界面几何），其参数在生成过程中根据迭代反馈进行演变，模仿自然抗体的精炼循环。 而不是固定的协议，这种自适应指导为每个目标发现个性化的优化策略。 我们的实验表明，这种方法：(1) 在无需预训练的情况下，为不同抗原类别发现最优的SE(3)等变指导策略，在整个优化过程中保持分子对称性；(2) 通过靶向特异性适应显著提高热点覆盖率和界面质量，实现治疗性抗体的平衡多目标优化；(3) 建立了一个迭代精炼的范式，每个抗体-抗原系统通过在线评估学习其独特的优化配置；(4) 在各种设计挑战中表现出有效的泛化能力，从小型表位到大蛋白界面，使针对个体目标的精准活动成为可能。 显示英文摘要

摘要： Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antigen's unique requirements. Inspired by B cell affinity maturation, where antibodies evolve through multi-objective optimization balancing affinity, stability, and self-avoidance, we propose the first biologically-motivated framework that leverages physics-based domain knowledge within an online meta-learning system. Our method employs multiple specialized experts (van der Waals, molecular recognition, energy balance, and interface geometry) whose parameters evolve during generation based on iterative feedback, mimicking natural antibody refinement cycles. Instead of fixed protocols, this adaptive guidance discovers personalized optimization strategies for each target. Our experiments demonstrate that this approach: (1) discovers optimal SE(3)-equivariant guidance strategies for different antigen classes without pre-training, preserving molecular symmetries throughout optimization; (2) significantly enhances hotspot coverage and interface quality through target-specific adaptation, achieving balanced multi-objective optimization characteristic of therapeutic antibodies; (3) establishes a paradigm for iterative refinement where each antibody-antigen system learns its unique optimization profile through online evaluation; (4) generalizes effectively across diverse design challenges, from small epitopes to large protein interfaces, enabling precision-focused campaigns for individual targets.