MSEDDI

MSEDDI: multi-scale embedding for predicting drug-drug interaction events

Dataset

• event.db contains the data we compiled from DrugBank 5.1.3 verision. It has 4 tables:
  1.drug contains 572 kinds of drugs and their features.
  2.event contains the 37264 DDIs between the 572 kinds of drugs.
  3.extraction is the process result of NLPProcess. Each interaction is transformed to a tuple: {mechanism, action, drugA, drugB}
  4.event_numer lists the kinds of DDI events and their occurence frequency.
• db1_drugs.csv contains 4 columns: id,name,smiles,fpt, which means drugbank id, drug name, SMILES and fingerprint.
• db1_drugs.smiles contains 572 SMILES of drugs.
• db1_events.csv contains the names and numbers of 65 events.
• db1_triplets.csv contains 74,528 DDIs, which are represented as drug,drug,label.
• db2_ddi.csv contains 5 columns: index,mechanism,action,drugA,drugB, which means sample order, mechanism of event, action of event, first drug in DDI, second drug in DDI.
• db2_drugs.csv contains 4 columns: id,name,smiles,fpt, which means drugbank id, drug name, SMILES and fingerprint.
• db2_drugs.smiles contains 1258 SMILES of drugs.
• db2_events.csv contains the names and numbers of 100 events.
• db2_triplets.csv contains 323,539 DDIs, which are represented as drug,drug,label.

Feature files

• *_afp_pcba.pkl: chemical structure graph embedding generated by AttentiveFP model.
• *_kges_transe.pkl: knowledge graph embedding calculated by TransE algorithm.
• *_mpnn_pcba.pkl: chemical structure graph embedding generated by MPNN model.
• *_smiles_vec.pkl: SMILES notation embedding calculated by word2vec.
• *_weave_pcba.pkl: chemical structure graph embedding generated by Weave model.

Code files

• custom_metrics.py: calculation method of evaluation metrics.
• ddi_datasets.py: data structure deformation before input to the model.
• loss.py: loss functions.
• models.py: MSEDDI model.
• train_cold_start.py: the entrance of case study program.

Requirement

• numpy=1.22.4
• pandas=1.4.2
• python=3.8.13
• scikit-learn=1.0.2
• torch=1.11.0+cu113
• torchaudio=0.11.0+cu113
• torchvision=0.12.0+cu113
  Use the following command to install all dependencies.

    pip install requirement.txt

Usage

Example Usage

    python train_cold_start.py

If you want to reproduct the case study, please run the other entrance:

    python train_case_study.py

Web service

http://121.36.221.79/MSEDDI/