Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This is especially important for clinically relevant G protein-coupled receptors (GPCRs), where therapeutic efficacy depends on functional directionality. Structure-based design methods optimize binding to static conformations and cannot represent non-reversible, directional effects or systematically distinguish agonist from antagonist behavior. To address this gap, we introduce Transition-Directed Discrete Diffusion for Allosteric Binder Design (TD3B), a sequence-based generative framework that designs binders with specified agonist or antagonist behavior via a directional transition control objective. TD3B combines a target-aware Direction Oracle, a soft binding-affinity gate, and amortized fine-tuning of a pre-trained discrete diffusion model, enabling targeted agonist and antagonist generation decoupled from binding affinity and unattainable by equilibrium-based or inference-only guidance baselines. The code and checkpoints are available at https://huggingface.co/ChatterjeeLab/TD3B.
Cite
@article{arxiv.2605.09810,
title = {TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation},
author = {Hanqun Cao and Aastha Pal and Sophia Tang and Yinuo Zhang and Jingjie Zhang and Pheng Ann Heng and Pranam Chatterjee},
journal= {arXiv preprint arXiv:2605.09810},
year = {2026}
}
Comments
Published as a Spotlight at ICML 2026 (Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea)