iGC：基于体细胞高频突变先验的强化学习抗体优化框架
iGC: Antibody Optimization via Reinforcement Learning over a Learned Somatic Hypermutation Prior
研究员把 B 细胞亲和力成熟过程搬进了强化学习，突变预测比最强基线翻倍，做抗体设计的可以看看这套两阶段方法。
iGC（in silico Germinal Centers）是一个两阶段强化学习框架，用于抗原特异性的抗体优化。第一阶段从 B 细胞谱系树的亲子对中学习体细胞高频突变（SHM）先验，并适配到目标亲和力成熟数据集。第二阶段以该先验初始化 RL 策略，用 KL 正则化约束优化抗原结合，并引入 Reward-Tax 元梯度目标（RewardNet 平滑亲和力景观、TaxNet 惩罚奖励滥用）防止重复高奖励替换。在突变位点预测上，该突变先验将最强基线成绩提升超过一倍，同时保持 SHM 序列结构和竞争性的奖励-多样性乘积。
iGC: Antibody Optimization via Reinforcement Learning over a Learned Somatic Hypermutation Prior
Antibody affinity maturation is a natural optimization process in which B cells acquire mutations through somatic hypermutation (SHM), undergo selection for improved antigen binding, and iteratively evolve toward higher affinity. Computationally modeling this process requires capturing both biologically plausible mutations and antigen-specific selection. However, existing mutation models do not directly optimize binding, while affinity optimization often neglects realistic mutation trajectories. We introduce iGC (in silico Germinal Centers), a two-stage reinforcement learning (RL) framework for antigen-specific antibody optimization. Stage 1 learns an SHM mutation prior from parent-child pairs in B-cell lineage trees and adapts it to a target affinity-maturation dataset. Stage 2 initializes an RL policy from this prior and optimizes antigen binding with Kullback-Leibler (KL) regularization toward the frozen prior. To prevent reward exploitation, including repetitive high-reward substitutions and structurally tolerated framework mutations, we introduce a Reward-Tax Meta-Gradient objective. RewardNet smooths the affinity landscape, while TaxNet penalizes reward overexploitation, jointly defining a learned meta-reward for policy optimization. We evaluate iGC on mutation prediction, SHM consistency, affinity optimization, and mutation diversity. The mutation prior more than doubles the strongest baseline in mutation-site prediction. Following RL optimization, iGC maintains SHM sequence structure. It also achieves a strong exploration-exploitation balance, yielding competitive reward-diversity products across candidate-selection cutoffs. These results demonstrate that iGC integrates biologically plausible mutation generation with antigen-specific affinity selection, providing an in silico framework for iterative antibody affinity maturation.