TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Hanqun Cao, Aastha Pal, Sophia Tang, Yinuo Zhang, Jingjie Zhang, Pheng-Ann Heng, Pranam Chatterjee
ICML 2026 spotlight
Abstract (source: OpenReview · © authors)
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 **T**ransition-**D**irected **D**iscrete **D**iffusion for allosteric**B**inder 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.
Keywords
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0). Phở does not host any PDF; links point back to the source.
Related
Discrete Diffusion with Physical Mass Constraints for \emph{De Novo} Peptide Sequencing
An Zeyu, Wanyu Lin
$\textit{De novo}$ peptide sequencing is a pivotal technique that directly reconstructs amino acid sequences from tandem mass spectrometry (MS/MS) data; it enables the…
Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang +4
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via…
FIDIA: Function-Informed Sequence Design via Inference-Aligned Policy Optimization
Minghan Li, Fengji Li, Yilin Tao, Yue Deng
Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly…