Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915085468827648 |
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| author | Espitia, Giovanny Pang, Yui Tik Gumbart, James C. |
| author_facet | Espitia, Giovanny Pang, Yui Tik Gumbart, James C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning Espitia, Giovanny Pang, Yui Tik Gumbart, James C. Machine Learning Artificial Intelligence Biomolecules 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. |
| title | Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2412.20329 |