MT-DTI

An official Molecule Transformer Drug Target Interaction (MT-DTI) model

• Author: Bonggun Shin
• Paper: Shin, B., Park, S., Kang, K. & Ho, J.C.. (2019). Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. Proceedings of the 4th Machine Learning for Healthcare Conference, in PMLR 106:230-248

Required Files

• Download data.tar.gz
  • This includes;
    • Orginal KIBA dataset from DeepDTA
    • tfrecord for KIBA dataset
    • Pretrained weights of the molecule transformer
    • Finetuned weights of the MT-DTI model for KIBA fold0
• Unzip it (folder name is data) and place under the project root

cd mtdti_demo
# place the downloaded file (data.tar.gz) at "mtdti_demo"
tar xzfv data.tar.gz

• These files sholud be in the right places

mtdti_demo/data/chembl_to_cids.txt
mtdti_demo/data/CID_CHEMBL.tsv
mtdti_demo/data/kiba/*
mtdti_demo/data/kiba/folds/*
mtdti_demo/data/kiba/mbert_cnn_v1_lr0.0001_k12_k12_k12_fold0/*
mtdti_demo/data/kiba/tfrecord/*.tfrecord
mtdti_demo/data/pretrain/*
mtdti_demo/data/pretrain/mbert_6500k/*

VirtualEnv

• install mkvirtualenv
• create a dti env with the following commands

mkvirtualenv --python=`which python3` dti
pip install tensorflow-gpu==1.12.0

Preprocessing

• If downloaded data.tar.gz, then you can skip these preprocessings

• Transform kiba dataset into one pickle file

python kiba_to_pkl.py 

# Resulted files
mtdti_demo/data/kiba/kiba_b.cpkl

• Prepare Tensorflow Record files

cd src/preprocess
export PYTHONPATH='../../'
python tfrecord_writer.py 

# Resulted files
mtdti_demo/data/kiba/tfrecord/*.tfrecord

FineTuning

• If downloaded data.tar.gz, then you can skip this finetuning

cd src/finetune
export PYTHONPATH='../../'
python finetune_demo.py 

Prediction

cd src/predict
export PYTHONPATH='../../'
python predict_demo.py