Multispecific antibodies offer transformative therapeutic potential by engaging multiple epitopes simultaneously, yet their efficacy is an emergent property governed by complex molecular architectures. Rational design is often bottlenecked by the inability to predict how subtle changes in domain topology influence functional outcomes, a challenge exacerbated by the scarcity of comprehensive experimental data. Here, we introduce a computational framework to address part of this gap. First, we present a generative method for creating large-scale, realistic synthetic functional landscapes that capture non-linear interactions where biological activity depends on domain connectivity. Second, we propose a graph neural network architecture that explicitly encodes these topological constraints, distinguishing between format configurations that appear identical to sequence-only models. We demonstrate that this model, trained on synthetic landscapes, recapitulates complex functional properties and, via transfer learning, has the potential to achieve high predictive accuracy on limited biological datasets. We showcase the model's utility by optimizing trade-offs between efficacy and toxicity in trispecific T-cell engagers and retrieving optimal common light chains. This work provides a robust benchmarking environment for disentangling the combinatorial complexity of multispecifics, accelerating the design of next-generation therapeutics.

翻译：多特异性抗体通过同时结合多个表位展现出变革性的治疗潜力，但其功效是由复杂分子结构所决定的涌现属性。理性设计常因无法预测结构域拓扑的细微变化如何影响功能结果而遭遇瓶颈，这一挑战因全面实验数据的稀缺而加剧。本文提出一种计算框架以部分填补该空白。首先，我们提出一种生成方法，用于创建大规模、真实的合成功能图谱，该图谱能捕捉生物活性依赖于结构域连接性的非线性相互作用。其次，我们提出一种图神经网络架构，该架构显式编码这些拓扑约束，能够区分在仅序列模型中看似相同的构型格式。我们证明，在合成图谱上训练的该模型能够复现复杂功能特性，并通过迁移学习在有限的生物数据集上具备实现高预测准确性的潜力。我们通过优化三特异性T细胞衔接器的功效-毒性权衡及检索最优通用轻链，展示了该模型的实用性。本研究为解析多特异性抗体组合复杂性提供了稳健的基准测试环境，可加速下一代治疗药物的设计。