FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
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TL;DR - FAR-DPO is an architecture-agnostic preference-optimization framework for generating cyclic peptides that satisfy coupled structural and biophysical constraints. It improves feasible-design yield and robustness across difficult drug targets without increasing the generation budget.
- Builds within-target preference pairs using feasibility-gated, multi-objective dominance.
- Uses difficulty-aware group-robust optimization to adaptively emphasize target groups with higher preference losses.
- On CPSea LNR, raises overall success from 46.89% to 57.79% for PepGLAD and from 47.96% to 49.57% for PepFlow.
- Improvements extend to the hardest target quartile and yield better best-per-target binding scores.
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FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
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TL;DR - FAR-DPO is an architecture-agnostic preference-optimization framework for generating cyclic peptides that satisfy coupled structural and biophysical constraints. It improves feasible-design yield and robustness across difficult drug targets without increasing the generation budget.
- Builds within-target preference pairs using feasibility-gated, multi-objective dominance.
- Uses difficulty-aware group-robust optimization to adaptively emphasize target groups with higher preference losses.
- On CPSea LNR, raises overall success from 46.89% to 57.79% for PepGLAD and from 47.96% to 49.57% for PepFlow.
- Improvements extend to the hardest target quartile and yield better best-per-target binding scores.