Disentangling multispecific antibody function with graph neural networks

TL;DR

This paper introduces a graph neural network framework trained on synthetic data to predict and optimize the functional properties of multispecific antibodies, addressing the challenge of complex topology-dependent activity prediction.

Contribution

It presents a novel generative approach for synthetic functional landscapes and a graph neural network architecture that captures topological constraints for antibody design.

Findings

Model recapitulates complex functional properties

Transfer learning enables high accuracy on limited data

Optimizes efficacy-toxicity trade-offs in antibody design

Abstract

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.