# Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2412.20329 · HTML: https://mlsanity.com/paper/2412.20329/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Abstract (arXiv)

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.