leoperezz / hp_modelprotein_3d_reinforcement_learning

Reinforcement Learning Project for HP 3D Model Protein Folding

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.

Problem Description

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.

Objective

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.

Example of the HP Model

Here is a basic visual representation of the HP Model:

Dependencies

• 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

Contributions to this project are welcome. If you wish to collaborate, please open an issue or send a pull request.