This project aims to apply reinforcement learning techniques to tackle the protein folding challenge in the HP Model. The HP Model is a simplified model used in computational biology to study protein folding.
The protein folding problem involves predicting the three-dimensional structure of a protein from its amino acid sequence. In the HP Model, the protein is represented as a sequence of hydrophobic (H) and polar (P) amino acids arranged on a lattice.
The main objective of this project is to develop a reinforcement learning agent that can find the optimal conformation of a protein in the HP Model, maximizing H-H contacts.
Here is a basic visual representation of the HP Model:
- Python 3.x
- torch==2.2.0+cu121
- numpy==1.24.3
- wandb==0.16.4
- torchvision==0.17.0+cu121
- gymnasium==0.29.1
Contributions to this project are welcome. If you wish to collaborate, please open an issue or send a pull request.