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This research introduces Pepti-drift, a novel generative framework that achieves competitive peptide design performance without relying on costly inference-time guidance. By decoupling generation efficiency from multi-property constraints, the method offers a scalable solution for high-throughput therapeutic peptide discovery where traditional models struggle with length-dependent safety biases. The work represents a significant step forward in making AI-driven drug modality generation both economically viable and rigorously evaluated.
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
arXiv:2606.27824v3 Announce Type: replace-cross Abstract: Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe-PepBench, a fixed-budget benchmark that jointly evaluates target binding and four major safety metrics on the same generated candidates. We reveal that peptide length is a major confounder of joint binding-safety evaluation: longer peptides tend toward stronger predicted binding but less favorable predicted safety. This creates an apparent trade-off and can bias comparisons among models with different output-length distributions. We report absolute Safe-Active yield and exact-length-matched gains to distinguish generative improvements from output-length effects. High Safe-Active yield remains challenging, while the strongest multi-property methods rely on costly inference-time guidance. We therefore introduce Pepti-drift, a one-step generation framework that incorporates attraction toward target-specific binders and repulsion from liability-associated regions, requiring a single latent refinement followed by parallel decoding without inference-time guidance. Across 88 held-out targets, Pepti-drift achieves an 18.37% predicted Safe-Active yield while retaining positive exact-length-matched gains. The resulting gains are competitive with multi-property-guided baselines while requiring 468 times lower generation cost, enabling scalable and fair high-throughput peptide design.
Abstract:Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe-PepBench, a fixed-budget benchmark that jointly evaluates target binding and four major safety metrics on the same generated candidates. We reveal that peptide length is a major confounder of joint binding-safety evaluation: longer peptides tend toward stronger predicted binding but less favorable predicted safety. This creates an apparent trade-off and can bias comparisons among models with different output-length distributions. We report absolute Safe-Active yield and exact-length-matched gains to distinguish generative improvements from output-length effects. High Safe-Active yield remains challenging, while the strongest multi-property methods rely on costly inference-time guidance. We therefore introduce Pepti-drift, a one-step generation framework that incorporates attraction toward target-specific binders and repulsion from liability-associated regions, requiring a single latent refinement followed by parallel decoding without inference-time guidance. Across 88 held-out targets, Pepti-drift achieves an 18.37% predicted Safe-Active yield while retaining positive exact-length-matched gains. The resulting gains are competitive with multi-property-guided baselines while requiring 468 times lower generation cost, enabling scalable and fair high-throughput peptide design.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.27824 [cs.LG]
(or arXiv:2606.27824v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.27824 Focus to learn more arXiv-issued DOI via DataCite
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