用注意力机制增强强化学习，解决三维蛋白质折叠难题。
Enhancing Reinforcement learning in 3-Dimensional Hydrophobic-Polar Protein Folding Model with Attention-based layers
- 将Transformer注意力嵌入DQN，构建3D蛋白质折叠决策模型。
- 在短序列上达成已知最优解，长序列接近最优结果。
- 适合对蛋白质折叠与深度强化学习交叉研究者参考。
基于Transformer的架构在序列建模中取得显著进展，但在二维/三维疏水-亲水（H-P）蛋白质折叠模型中的应用仍较少。本文将注意力机制融入深度Q网络（DQN），用于解决3D H-P蛋白质折叠问题。系统将折叠过程建模为受强化环境约束的自避行走，并设计基于有利疏水相互作用的奖励函数。为提升性能，方法引入对称性破缺约束、双重和双Q学习，以及优先级经验回放，聚焦关键状态转移的学习。在标准基准序列上的实验表明，该方法在较短序列上实现多个已知最优解，在较长链上获得近似最优结果。本研究验证了注意力增强型强化学习在蛋白质折叠中的潜力，并构建了适用于三维格点模型的Transformer-based Q网络原型。
原文摘要 · Abstract (English)
Transformer-based architectures have recently propelled advances in sequence modeling across domains, but their application to the hydrophobic-hydrophilic (H-P) model for protein folding remains relatively unexplored. In this work, we adapt a Deep Q-Network (DQN) integrated with attention mechanisms (Transformers) to address the 3D H-P protein folding problem. Our system formulates folding decisions as a self-avoiding walk in a reinforced environment, and employs a specialized reward function based on favorable hydrophobic interactions. To improve performance, the method incorporates validity check including symmetry-breaking constraints, dueling and double Q-learning, and prioritized replay to focus learning on critical transitions. Experimental evaluations on standard benchmark sequences demonstrate that our approach achieves several known best solutions for shorter sequences, and obtains near-optimal results for longer chains. This study underscores the promise of attention-based reinforcement learning for protein folding, and created a prototype of Transformer-based Q-network structure for 3-dimensional lattice models.
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