FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design

• URL: http://arxiv.org/abs/2608.19808v1
• Date: Thu, 20 Aug 2026 09:00:26 GMT
• Title: FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
• Abstract summary: We propose FAR-DPO, an architecture-agnostic framework that steers generative models toward feasible cyclic peptide designs.<n> FAR-DPO integrates feasibility-aware preference construction with difficulty-aware group-robust optimization.<n>Under a fixed generation budget, FAR-DPO increases overall success rate from 46.89% to 57.79% on PepGLAD and from 47.96% to 49.57% on PepFlow.
• Score: 24.36623791256783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models from linear to cyclic peptide design remains challenging, as cyclization sharply restricts the feasible design space through coupled geometric and biophysical constraints. Moreover, limited training data has led existing approaches to rely largely on zero-shot generation or post hoc filtering, resulting in low yields of feasible designs and limited control over multi-objective trade-offs. To address these limitations, we propose FAR-DPO (Feasibility-Aware and Robust Direct Preference Optimization), an architecture-agnostic framework that steers generative models toward structurally and biophysically feasible cyclic peptide designs, particularly for challenging targets. FAR-DPO integrates feasibility-aware preference construction with difficulty-aware group-robust optimization. Specifically, it constructs within-target preference pairs through feasibility-gated multi-objective dominance and adaptively reweights predefined difficulty groups according to their current preference losses. On the CPSea LNR benchmark, under a fixed generation budget, FAR-DPO increases overall success rate from 46.89% to 57.79% on PepGLAD and from 47.96% to 49.57% on PepFlow. These gains also extend to the hardest target quartile and are accompanied by more favorable best-per-target binding scores. Together, these results demonstrate FAR-DPO's effectiveness in improving feasibility and target-wise robustness.

Related papers

• APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems [64.56252173115246]
  We propose (Atomic Policy Optimization) a fully unsupervised alignment framework that eliminates the need for ground-truth reference structures.<n>APO adapts group-relative policy optimization to 3D atomic environments, utilizing a novel dual-reward mechanism.<n>Our results suggest that intrinsic physical consistency can serve as a superior guide for alignment compared to noisy, supervised coordinate matching.
  arXiv  Detail & Related papers  (2026-07-30T17:21:58Z)
• Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization [85.4863852505905]
  We introduce unsupervised reward optimization of protein language models (PLMs)<n>Our key insight is that task-agnostic rewards, which combine intrinsic model uncertainty with semantic consistency informed by protein representation models, exhibit strong correlation with controllability measures across base models and temperature regimes.<n>Our framework provides a scalable pathway toward controllable biomolecular design in settings where labeled preferences or experimental feedback are scarce or unavailable.
  arXiv  Detail & Related papers  (2026-06-17T11:42:01Z)
• Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling [22.66917124518062]
  Existing generative approaches often rely on costly post-hoc processing during Sampling or require carefully curated datasets during training.<n>We propose FTDiff, a reinforcement learning fine-tuning framework tailored for diffusion-based molecular generation under structural constraints.<n>We show that FTDiff consistently outperforms prior methods, without requiring expensive post-hoc optimization or intricate data engineering.
  arXiv  Detail & Related papers  (2026-05-31T13:11:48Z)
• Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment [19.34182674159143]
  Protein sequence design must balance designability, defined as the ability to recover a target backbone.<n>Existing approaches address these properties through post hoc mutation, inference-time biasing, or retraining on property-specific subsets.<n>We introduce ProtAlign, a multi-objective preference alignment framework that fine-tunes inverse folding models to satisfy diverse developability objectives.
  arXiv  Detail & Related papers  (2026-03-06T10:57:51Z)
• Protein Inverse Folding From Structure Feedback [78.27854221882572]
  We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
  arXiv  Detail & Related papers  (2025-06-03T16:02:12Z)
• Improving Protein Sequence Design through Designability Preference Optimization [22.037870784317885]
  We redefine the training objective by steering sequence generation toward high designability.<n>We introduce Residue-level Designability Preference Optimization (ResiDPO)<n>This enables direct improvement in designability while preserving regions that already perform well.
  arXiv  Detail & Related papers  (2025-05-30T23:02:51Z)
• Direct Preference Optimization for Primitive-Enabled Hierarchical Reinforcement Learning [75.9729413703531]
  DIPPER is a novel HRL framework that formulates hierarchical policy learning as a bi-level optimization problem.<n>We show that DIPPER achieves up to 40% improvement over state-of-the-art baselines in sparse reward scenarios.
  arXiv  Detail & Related papers  (2024-11-01T04:58:40Z)
• Decomposed Direct Preference Optimization for Structure-Based Drug Design [47.561983733291804]
  We propose DecompDPO, a structure-based optimization method to align diffusion models with pharmaceutical needs. DecompDPO can be effectively used for two main purposes: fine-tuning pretrained diffusion models for molecule generation across various protein families, and molecular optimization given a specific protein subpocket after generation.
  arXiv  Detail & Related papers  (2024-07-19T02:12:25Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
  DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model. We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
  arXiv  Detail & Related papers  (2024-03-07T02:53:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.