Disentangling multispecific antibody function with graph neural networks
This work addresses the bottleneck in rational design of multispecific antibodies for therapeutic applications, offering a benchmarking environment to accelerate the development of next-generation therapeutics, though it is incremental as it builds on existing graph neural network methods.
The paper tackled the problem of predicting how subtle changes in domain topology affect the functional outcomes of multispecific antibodies, a challenge due to scarce experimental data, by introducing a computational framework with a generative method for synthetic functional landscapes and a graph neural network that encodes topological constraints, demonstrating its utility in optimizing efficacy-toxicity trade-offs and retrieving optimal common light chains.
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.