MFAE

MFAE: Multilevel Feature Aggregation Enhanced Drug-Target Affnity Prediction for Drug Repurposing against Colorectal Cancer. This research introduces the first comprehensive drug-target affnity dataset for CRC-specific targets, P2X4 and mTOR. Utilizing a novel deep learning MFAE model, specific inhibitors are systematically sourced from ChEMBL. The meticulously fine-tuned model then screens FDA-approved drugs, effectively pinpointing potential lead compounds for CRC treatment.

File list

• Data: the processed dataset of CRC dataset.
• Model: the trained model weights of MFAE.
• Result: the evaluate result of model.
• CRC_data.csv: the original CRC dataset.
• readme.md: the readme file.
• split_dataset.py: split dataset into train, val, test with 5 folds on Data folder. And the esamble dataset is in Data/bagging_data folder.
• training.py: train the model with the dataset in Data folder.
• test.py: test the model with the dataset in Data folder.
• utils.py: some useful functions.
• model.py: the code about model.
• dataset.py: the code about dataset.

Requirements

numpy==1.24.3
pandas==1.5.3
scikit_learn==1.3.0
scipy==1.10.1
torch==1.12.1
torch_geometric==2.3.1
torchvision==0.13.1
tqdm==4.65.0

Run Code

Step 1: Split the dataset into train, val, test with 5 folds on Data folder. And the esamble dataset is in Data/bagging_data folder.

python split_dataset.py

Step 2: Train the model with the dataset in Data folder.

python train.py

Step 3: Test the trained model.

python test.py