AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization

• URL: http://arxiv.org/abs/2506.07035v1
• Date: Sun, 08 Jun 2025 07:59:09 GMT
• Title: AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
• Authors: Zixuan Jiang, Renjing Xu,
• Abstract summary: Deciphering protein function remains a fundamental challenge in protein representation learning.<n>We propose AnnoDPO, a novel multi-modal framework for protein function prediction.<n>Our methodology addresses the dual challenges of annotation scarcity and imbalance through preference-aligned training objectives.
• Score: 1.8651695783984825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal framework for protein function prediction that leverages Direct Preference Optimization (DPO) to enhance annotation learning. Our methodology addresses the dual challenges of annotation scarcity and category imbalance through preference-aligned training objectives, establishing a new paradigm for biological knowledge integration in protein representation learning.

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