mlsanityData to 7 Oct 2026

TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation

arXiv 2605.09810 ↗First posted 10 May 2026q-bio.BM cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 63 references that are in the map. First posted on arXiv 10 May 2026.

Builds on

15 of 63 references.

References · 15

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2022Classifier-Free Diffusion Guidancecited by 5,717
  3. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  4. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  5. 2024Simple and Effective Masked Diffusion Language Modelscited by 800
  6. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distributioncited by 712
  7. 2024Simplified and Generalized Masked Diffusion for Discrete Datacited by 606
  8. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  9. 2024Unlocking Guidance for Discrete State-Space Diffusion and Flow Modelscited by 124
  10. 2024Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Designcited by 72
  11. 2024Particle Denoising Diffusion Samplercited by 63
  12. 2024PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusioncited by 35
3 more
  1. 2025Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Designcited by 13
  2. 2025TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusioncited by 13
  3. 2025AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidancecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  2. 2024BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
  3. 2024General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
  4. 2024Binding-Adaptive Diffusion Models for Structure-Based Drug Design
  5. 2025UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
  6. 2025Controllable protein design with particle-based Feynman-Kac steering
  7. 2026Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
  8. 2024BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design