用深度强化学习预测蛋白质三维结构，效率显著提升。
Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
- 结合随机投影与可训练层的混合模型，减少训练次数
- 长序列用注意力LSTM匹配最优能量值
- 稳定强化学习框架提升收敛速度，适合蛋白结构研究
我们针对3D疏水-极性格点模型中的蛋白质结构预测问题，提出两种新型深度学习架构。对于短于36个氨基酸的蛋白质，采用基于池化机制的混合模型，结合固定随机投影与可训练深层网络，在训练次数减少25%的情况下达到最优构象。对于更长序列，使用带多头注意力的长短期记忆网络，实现与当前最优能量值相当的结果。两种架构均基于稳定的深度Q-learning框架，结合经验回放与目标网络，显著提升训练效率并一致获得最优构象。
原文摘要 · Abstract (English)
We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes. For longer sequences, we employ a long short-term memory network with multi-headed attention, matching best-known energy values. Both architectures leverage a stabilized Deep Q-Learning framework with experience replay and target networks, demonstrating consistent achievement of optimal conformations while significantly improving training efficiency compared to existing methods.
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