先确定抗体结合位点，再精准设计氨基酸，提升抗体生成精度。
ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning
- 分三步设计：识别接触面、预测结合位置、注入靶向特征
- 在三个数据集上提升11%~16%的天然接触恢复率
- 适合抗体药物设计与结构生物学研究者
现有计算抗体可变区设计方法将识别结合位置与选择氨基酸混为一谈，导致模型需通过统一消息传递隐式学习接触关系，稀释了抗原信号。本文提出ConTact，一种先接触后生成的架构，包含三个级联阶段：学习表面互补指纹、预测抗体-抗原接触点、将接触门控的抗原特征注入预测头。采用距离偏置交叉注意力模块引入空间邻近先验，使用接触加权交叉熵损失集中梯度至关键位置，引入远距接触恢复损失使设计环与天然表位对齐。在ChiMERa-Bench数据集上，ConTact在所有三个划分上均优于基线，结合界面与结构指标（主链RMSD、天然接触比例、界面RMSD、DockQ、表位F1）全面领先，天然接触恢复率提升11%~16%，序列恢复达到最优水平。源代码见：https://github.com/mansoor181/ConTact.git。
原文摘要 · Abstract (English)
Computational antibody CDR design methods condition on antigen structure to generate binding loops. Yet, the existing architectures conflate two fundamentally distinct sub-problems: identifying which CDR positions will contact the antigen, and selecting amino acids at those positions. This forces models to learn contact reasoning implicitly through uniform message passing, diluting antigen signal across all positions equally. We introduce ConTact, a contact-then-act architecture that explicitly decomposes CDR design into three cascaded stages: learning surface complementarity fingerprints, predicting CDR-antigen contacts, and injecting contact-gated antigen features into the prediction head. A distance-biased cross-attention module encodes geometric priors favoring spatial neighbors, a contact-weighted cross-entropy loss concentrates gradient signal on binding-critical positions, and a standoff-aware contact-recovery loss places the designed loop against its native epitope. On the ChiMERa-Bench dataset, ConTact surpasses the baselines on the binding interface and structural metrics (backbone RMSD, fraction of native contacts, interface RMSD, DockQ, and epitope F1) on all three splits, improving native-contact recovery by 11 to 16%, while matching the best sequence recovery. The source code is available at: https://github.com/mansoor181/ConTact.git
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