PepDiT: Structure-Tokenized Diffusion Transformers for Target-Spot Driven Antimicrobial Peptide Generation

The rapid emergence of multidrug-resistant pathogens represents an urgent global health challenge, necessitating the de novo design of selective antimicrobial peptides (AMPs) and target-specific mini-binders. However, computational peptide design remains bottlenecked by three fundamental limitations: (1) $\text{Structural Representation Deficit:}$ short polypeptides ($\sim 10$–$50$ residues) exhibit pronounced conformational plasticity and lack stable hydrophobic globular cores, rendering conventional protein structural tokenizers ineffective; (2) $\text{Target-Agnostic Generation:}$ existing generative models sample sequences blindly from global natural distributions without spatial-chemical guidance toward pathogen-specific surface interaction hotspots; and (3) $\text{The Potency-Toxicity Trade-off:}$ unguided generation often yields membrane-disruptive peptides with severe mammalian cytotoxicity and red blood cell hemolysis. To overcome these challenges, we introduce PepDiT, a unified structure-tokenized diffusion transformer framework for target-spot-driven AMP and mini-binder design with multi-objective preference alignment. We first curate $\mathrm{PepDB}$-1.2M, a dataset of 1.2 million diverse polypeptide conformations, and train PepTokenizer, an $\mathrm{SE}(3)$-equivariant discrete geometric vector quantizer with torsional-dihedral codebooks that captures peptide backbone dynamics with sub-Angstrom precision. Building upon these discrete structural tokens, we formulate $\text{PepDiT}$, a multi-track Diffusion Transformer that co-diffuses continuous sequence embeddings and quantized 3D geometric tokens. PepDiT incorporates a geometric Target-Spot Cross-Attention Module that conditions generative trajectories directly on 3D electrostatic, hydrophobic, and steric surface hotspots of pathogenic targets (such as outer-membrane BamA complexes, LPS Lipid A clusters, and Penicillin-Binding Proteins). Furthermore, we establish a Multi-Objective Direct Preference Optimization (Pep-DPO) post-training protocol to navigate the Pareto frontier between antimicrobial potency (pMIC), low hemolysis ($H{C}_{50}$), serum stability, and target binding affinity ($\Delta G_{bind}$). To systematically evaluate computational polypeptide models, we establish PepBenchmark, a standardized 12-task benchmark spanning structural reconstruction, broad-spectrum bacterial killing (across 7 critical ESKAPE and fungal pathogens), safety indices, and target pocket engagement. Extensive experiments demonstrate that PepDiT establishes state-of-the-art performance across all 12 tasks, achieving an unprecedented Selectivity Index ($\mathrm{SI}>240$), an $8.4\times$ improvement in target-spot binding hit rate, and picomolar-to-nanomolar binding affinities against challenging antimicrobial targets.