This repository contains the MutDTA deep learning model, which is a transfer learning model designed to predict the impact of mutations on DTA and to elucidate resistance mechanisms effectively. The model is implemented in python and pytorch for accurate affinity predictions.

torch 1.8.0
python 3.8.18
numpy 1.22.0
pandas 1.3.1
scikit-learn 0.24.0
scipy 1.10.1

All datasets of our preprocessing data:

1. Train and test on the demo data

python main.py --dataset <data_name> --learn_rate <learn_rate> --epochs <epochs> --batch_size <batch_size>

For example:

python main.py --dataset platinum --learn_rate 0.00001 --epochs 100 --batch_size 128

2. Train and test on your own data

If you want to run MutDTA on your data, just preprocess your data into the specified form as follows:

and then, you can use MutDTA to train or test your data using the following command:

python main.py --dataset <your_dataset_name> --learn_rate 0.00001 --epochs 100 --batch_size 128