TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918493801152512 |
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| author | Cao, Hanqun Pal, Aastha Tang, Sophia Zhang, Yinuo Zhang, Jingjie Heng, Pheng Ann Chatterjee, Pranam |
| author_facet | Cao, Hanqun Pal, Aastha Tang, Sophia Zhang, Yinuo Zhang, Jingjie Heng, Pheng Ann Chatterjee, Pranam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09810 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation Cao, Hanqun Pal, Aastha Tang, Sophia Zhang, Yinuo Zhang, Jingjie Heng, Pheng Ann Chatterjee, Pranam Biomolecules Machine Learning 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. |
| title | TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2605.09810 |