README.md

Usage

Environment Configuration

conda create -n premota python=3.8
conda activate premota
pip install -r requirements.txt

Classification model pre-training

Pre-training code for the classification model is located in './src'. Usage details follow:

1. Prepare your classification data

The data used for pre-training is located in the ./datasets/CPI_data_cls directory. You can use the provided CPI_data_cls dataset or prepare your own dataset. The data can be downloaded from this link and should be placed in the raw_data folder under ./datasets/CPI_data_cls. Run ./src/data/data_process.py to generate the training and validation datasets for the classification model.

2. Train the classification model

Run ./src/train.py to train the classification model. The trained models will be saved in the ./src/model_save/CPI_data_cls directory. For each run, the best model is saved as bach1LR0.0001random2024esm2.pt. You can use this model for subsequent affinity fine-tuning tasks. Alternatively, you can download the pre-trained model from this link and place it in the ./src/model_save/CPI_data_cls directory.

NOTICE : The pre-trained classification model is available for affinity fine-tuning, you can also retrain a new model with custom data

Affinity fine-tuning model

Affinity fine-tuning code is located in './regression_multitask'. Usage details follow:

1. Prepare your affinity data

Data used for fine-tuning is located in the ./dataset_reg_multitask/ directory. Different targets data are located in different subdirectories. You can use the provided data or prepare your own data.

2. Fine-tune the regression model

To fine-tune the model, run ./regression_multitask/train_reg_fintune.py. The fine-tuned models will be saved in the ./regression_multitask/model_fintune_save/ directory. For each prediction target, the best model is saved as ratio_0.9batch128LR_1e-4random_0_esm2.pt within the respective target folder. You can also download the pre-trained models from this link and place them in the ./regression_multitask/model_fintune_save/ directory. Alternatively, to train the model from scratch, run ./regression_multitask/train_reg_train.py. The trained models will be saved in the ./regression_multitask/model_train_save/ directory.

Predict compound-off-target binding affinity

Run ./regression_multitask/predict_drugs.py or ./regression_multitask/predict_multidata_adr.py to predict the compound-off-target binding affinity.