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README.md

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-# Token-Level Guided Discrete Diffusion for Membrane Protein Design
+---
+license: cc-by-nc-nd-4.0
+---
 
-![MemDLM diagram](./memdlm_schematic.png)
+<h1 align='center'>Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design</h1>
 
+<div align="center">
+  <a href="https://shreygoel09.github.io/" target="_blank">Shrey Goel</a><sup>1</sup>&ensp;<b>&middot;</b>&ensp;
+  <a href="https://www.chatterjeelab.com/" target="_blank">Pranam Chatterjee</a><sup>2<sup>
+  <br>
+  <p style="font-size: 16px;">
+  <sup>1</sup> Duke University &emsp; 
+  <sup>2</sup> University of Pennsylvania &emsp; 
+</div>
+    
+<div align="center">
+ <a href="https://arxiv.org/abs/2410.16735"><img src="https://img.shields.io/badge/Arxiv-2506.09007-red?style=for-the-badge&logo=Arxiv" alt="arXiv"/></a>
 
-arXiv preprint: ...
+</div>
 
-Reparameterized diffusion models (RDMs) have recently matched autoregressive methods in protein generation, motivating their use for challenging tasks such as designing membrane proteins, which possess interleaved soluble and transmembrane (TM) regions.  
 
-We introduce ***Membrane Diffusion Language Model (MemDLM)***, a fine-tuned RDM-based protein language model that enables controllable membrane protein sequence design. MemDLM-generated sequences recapitulate the TM residue density and structural features of natural membrane proteins, achieving comparable biological plausibility and outperforming state-of-the-art diffusion baselines in motif scaffolding tasks by producing:  
 
-- Lower perplexity  
-- Higher BLOSUM-62 scores  
-- Improved pLDDT confidence  
+![MadSBM Diagram](./MadSBM_Figure.png)
 
-To enhance controllability, we develop ***Per-Token Guidance (PET)***, a novel classifier-guided sampling strategy that selectively solubilizes residues while preserving conserved TM domains. This yields sequences with reduced TM density but intact functional cores.  
+Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Existing discrete diffusion and flow-based methods rely on reversing fixed corruption processes or following prescribed probability paths, which can force generation through low-likelihood regions and require many sampling steps.
 
-Importantly, MemDLM designs validated in TOXCAT β-lactamase growth assays demonstrate successful TM insertion, distinguishing high-quality generated sequences from poor ones.  
+We introduce **Minimal-Action Discrete Schrödinger Bridge Matching (MadSBM)**, a rate-based generative framework for peptide design that formulates generation as a controlled continuous-time Markov process on the amino-acid edit graph. To produce probability trajectories that remain within high-likelihood sequence neighborhoods throughout generation, MadSBM:
 
-Together, our framework establishes the first experimentally validated diffusion-based model for rational membrane protein generation, integrating *de novo* design, motif scaffolding, and targeted property optimization.  
+1. Defines generation relative to a biologically informed reference process derived from pretrained protein language model logits.
+2. Learns a time-dependent control field that biases transition rates to induce low-action transport paths from a masked prior to the data distribution.
 
+Finally, we introduce an objective-guided sampling procedure that steers MadSBM generation toward specific functional targets, representing—to our knowledge—the first application of discrete classifier guidance within a Schrödinger bridge-based generative framework.
 
 
 ## **Repository Authors**