This project was carried out at AICP2022(Artificial Intelligence Challengers Program).
📂 More information can be found here.
- This study shows that the combination of molecular dynamics (MD) and deep learning (DL) can be used to search eco-friendly hydrate inhibitors.
- The MD simulation results provided detailed information about the molecules and the hydrate structures, and numerous maximum pulling force data.
- The graph neural network (GNN) approaches were used to identify the most effective inhibitors according to their molecular graph data.
- We believe that the combination of these methods can help to find the best possible hydrate inhibitors and predict the pulling force faster and more accurately.
- DataPreprocessing.ipynb : Code for processing raw data obtained through MD simulation into graph structure data is described
- Code.ipynb : A graph neural network model capable of learning graph structures and code for learning methods are described. It also contains a code that can visualize the binding importance of each molecule.