Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
Authors/Creators
- 1. Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore
- 2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- 3. EXN SIA, Jurmala, Latvia
- 4. NVIDIA Seattle Robotics Lab, Redmond, 98052, WA, United States
- 5. Department of Physics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China
Description
Conformation database for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"
- DOI of Physica A publication: https://doi.org/10.1016/j.physa.2022.128395
- GitHub source code: https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction
This conformation database shows the distinct conformations of best-known and next best energies:
├── 20merA
│ ├── 20merA_E8_set
│ ├── 20merA_E9_set
│ ├── confs_20merA_E8.txt
│ └── confs_20merA_E9.txt
├── 20merB
│ ├── 20merB_E10_set
│ ├── 20merB_E9_set
│ ├── confs_20merB_E10.txt
│ └── confs_20merB_E9.txt
├── 24mer
│ ├── 24mer_E8_set
│ ├── 24mer_E9_set
│ ├── confs_24mer_E8.txt
│ └── confs_24mer_E9.txt
├── 25mer
│ ├── 25mer_E7_set
│ ├── 25mer_E8_set
│ ├── confs_25mer_E7.txt
│ └── confs_25mer_E8.txt
├── 36mer
│ ├── 36mer_E13_set
│ ├── 36mer_E14_set
│ ├── confs_36mer_E13.txt
│ └── confs_36mer_E14.txt
├── 48mer
│ ├── 48mer_E22_set
│ ├── 48mer_E23_set
│ ├── confs_48mer_E22.txt
│ └── confs_48mer_E23.txt
└── 50mer
├── 50mer_E20_set
├── 50mer_E21_set
├── confs_50mer_E20.txt
└── confs_50mer_E21.txt
Notes
Files
20merA.zip
Files
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