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De novo peptide design methods traditionally couple generation to 3D structure prediction, limiting throughput to seconds or hours per candidate. Here we present LigandForge, a discrete diffusion model that generates binding peptide sequences in a single forward pass from receptor pocket geometry alone — no structure prediction, inverse folding, or iterative refinement at inference. LigandForge produces over 700 sequences per second on a single GPU (peak >1,000), a throughput advantage exceeding 10,000-fold over BoltzGen and 1,000,000-fold over BindCraft. We generated 490,691 peptides across 150 receptor targets and validated 16,475 by Boltz-2 structure prediction. DeltaForge, a Rust-based thermodynamic scoring engine calibrated against experimental binding data (Pearson r = 0.83 on the PPB-Affinity peptide benchmark), identified predicted sub-100 nM binders across 85 of 116 scored targets (73%), sub-10 nM across 62 (53%), and sub-1 nM across 35 (30%). In a five-target head-to-head on historically difficult targets (TNF-α, PD-L1, VEGF-A, IL-7Rα, HER2), LigandForge generated 150,000 candidates in 3.4 minutes and produced predicted sub-100 nM binders against all five targets (23 total from 576 folded structures), compared to 1 of 5 targets for BoltzGen (2 hits from 100 designs) and 0 for BindCraft (0 pipeline-accepted designs). DSSP analysis of 7,585 designed peptides revealed that LigandForge produces structurally diverse folds (45% helical, 28% β-sheet) compared to the helix-dominated outputs of backbone-sampling methods (BoltzGen 73%, BindCraft 90% helical). LigandForge also generated peptides embedding within orthosteric pockets of aminergic GPCRs with no evolutionary precedent for peptide ligands, and natively targets heterodimeric and homomultimeric receptors including the CD8A–CD8B heterodimer (60.5% elite structural confidence, 19.5% simultaneous dual-chain engagement), the CD3D–CD3E signaling complex, and the KIT receptor tyrosine kinase homodimer in vacancy pairing mode (59% bivalent engagement, ΔG < −26 kcal/mol). These results demonstrate that thermodynamic knowledge compiled into model weights during training can replace iterative structure prediction at inference, enabling a paradigm shift from structure-dependent optimization of individual candidates to structure-free exploration of sequence space at scale — with comparable or superior predicted binding quality, broader structural diversity, and access to target classes beyond the reach of backbone-sampling methods.