# GMJRL_1

**Repository Path**: Tomhappy/GMJRL_1

## Basic Information

- **Project Name**: GMJRL_1
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2026-02-01
- **Last Updated**: 2026-02-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# GMJRL
GMJRL: Geometry-enhanced Multi-scale Joint Representation Learning for Drug-Target Interaction Prediction

##### Dependencies：

- python = 3.8.18
- pytorch = 1.12.1
- torchvision = 0.13.1
- cudatoolkit = 11.3.1
- rdkit = 2023.9.1
- torch-cluster = 1.6.0+pt112cu113
- torch-scatter = 2.10+pt112cu113
- torch-sparse = 0.6.16+pt112cu113
- torch-spline-conv = 1.2.1+pt112cu113
- torch-geometric = 2.4.0
- networkx = 3.1
- torchdrug = 0.2.1
- bio = 1.6.2
- openpyxl = 3.1.2

##### Using

1. `Data` stores four datasets.
2. `protein_structrue` stores target graphs. 
3. `create_drug_feat.py` is used to ​​extract structural features from drug molecules (represented as SMILES)​​ and convert them into a format suitable for processing by ​​Graph Neural Networks​​. 
4. `generate_protein_structure.py` is used to generate target graphs.
5. `main.py` trains GMJRL model.

##### Training

If you are using our data, you need to first run `generate_protein_structure.py` to generate target graphs, and then run `main.py` directly.

For a new dataset, you need to prepare the following files:
1. `drugs.xlsx`: This file stores the SMILES string information of the drugs.
2. `targets.xlsx`: This file stores the Fasta sequence information of the targets.
3. `dti_mat.xlsx`: This file stores the interaction information between drugs and targets.
4. `protein_structrue`: This file stores target graphs.

Next, you need to run the following scripts:
1. Run `create_drug_feat.py` to extract structural features and convert them into a format suitable for processing by Graph Neural Networks.
2. Run `generate_protein_structure.py` to generate target graphs.
3. Run `main.py` to train the GMJRL model.